{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download OCCA data from [here](http://www.ecco-group.org/products.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xray\n",
    "import gsw\n",
    "import os\n",
    "from scipy import io\n",
    "from scipy import fftpack as fft\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'oceanmodes.baroclinic' from '/home/takaya/oceanmodes/oceanmodes/baroclinic.py'>"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from oceanmodes import baroclinic\n",
    "reload(baroclinic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basedir = '/data/scratch/takaya/OCCA/annual'\n",
    "fname_eta_surf = os.path.join(basedir, 'DDetan.0406annclim.nc')\n",
    "fname_potP_bot = os.path.join(basedir, 'DDphibot.0406annclim.nc')\n",
    "fname_potP = os.path.join(basedir, 'DDphihyd.0406annclim.nc')  # potentail pressure: P/rho\n",
    "fname_rhoanom = os.path.join(basedir, 'DDrhoan.0406annclim.nc')\n",
    "fname_u = os.path.join(basedir, 'DDuvel.0406annclim.nc')\n",
    "fname_v = os.path.join(basedir, 'DDvvel.0406annclim.nc')\n",
    "fname_s = os.path.join(basedir, 'DDsalt.0406annclim.nc')\n",
    "fname_t = os.path.join(basedir, 'DDtheta.0406annclim.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nc_eta_surf = xray.open_dataset(fname_eta_surf)\n",
    "nc_potP_bot = xray.open_dataset(fname_potP_bot)\n",
    "nc_potP = xray.open_dataset(fname_potP)\n",
    "nc_rhoanom = xray.open_dataset(fname_rhoanom)\n",
    "nc_u = xray.open_dataset(fname_u)\n",
    "nc_v = xray.open_dataset(fname_v)\n",
    "nc_s = xray.open_dataset(fname_s)\n",
    "nc_t = xray.open_dataset(fname_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eta_s = nc_eta_surf.etan[0]\n",
    "potP_b = nc_potP_bot.phibot[0]\n",
    "potP = nc_potP.phihyd[0]\n",
    "rho_anom = nc_rhoanom.rhoanoma[0]\n",
    "u = nc_u.u[0]\n",
    "v = nc_v.v[0]\n",
    "sal = nc_s.salt[0]\n",
    "theta = nc_t.theta[0]\n",
    "lat_t = nc_s.Latitude_t\n",
    "lon_t = nc_s.Longitude_t\n",
    "lat_v = nc_v.Latitude_v\n",
    "lon_u = nc_u.Longitude_u\n",
    "z_t = nc_s.Depth_c\n",
    "z_u = nc_u.Depth_u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derive geostrophic velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -18.28566933  -18.42925072  -18.56686783  -18.69758797  -18.8199482\n",
      "  -18.93309021  -19.0370903   -19.13277435  -19.2214489   -19.30424881\n",
      "  -19.38214302  -19.45638275  -19.5287571   -19.60124207  -19.67517853\n",
      "  -19.74976158  -19.81965637  -19.87224197  -19.88490105  -19.8229351\n",
      "  -19.63894081  -19.27416992  -18.66243935  -17.73610878  -16.43343353\n",
      "  -14.70406342  -12.5130415    -9.8430357    -6.68998671   -3.05775762\n",
      "    1.05009902    5.6520257    10.82610703   16.75936127   23.78192139\n",
      "   32.35907364   43.04357147   56.42181015   73.0890274    93.65625763\n",
      "  118.77246857  149.14060974  185.52250671  228.73904419  279.668396\n",
      "  339.24563599  408.45767212  488.33166504           nan           nan] [ -18.22412682  -18.36836433  -18.50669861  -18.63830948  -18.76181602\n",
      "  -18.87623787  -18.98143768  -19.07815361  -19.16767693  -19.25122261\n",
      "  -19.32982254  -19.40476799  -19.47787857  -19.55117226  -19.62602615\n",
      "  -19.70166016  -19.77273369  -19.82655525  -19.84037781  -19.77937889\n",
      "  -19.59609413  -19.23180389  -18.62044907  -17.69431877  -16.39188194\n",
      "  -14.6629715   -12.47225189   -9.80260658   -6.65005779   -3.01817536\n",
      "    1.08946741    5.69101238   10.86427784   16.79656219   23.81822777\n",
      "   32.39438629   43.07766724   56.45440674   73.11988068   93.6852417\n",
      "  118.79946899  149.16531372  185.54457092  228.75811768  279.68386841\n",
      "  339.25689697  408.46484375  488.33587646           nan           nan]\n"
     ]
    }
   ],
   "source": [
    "print potP.values[:, 20, 0], potP.values[:, 20, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160,) (360,) (50, 160, 359) (50,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe996b0c90>]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eq6p3AY8DBwCS3ADcAVwPvB84mGTTH+CrjTl/Hvbtg4UFWFqCw4ddzpF0sXVDv6q+Afxo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\nhr4kzRFDX5LmyP8HT3le4UkQiykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe99879d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print lat_t.shape, lon_t.shape, potP[:,:,1:].shape, z_t.shape\n",
    "plt.plot(lon_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# P = gsw.p_from_z(-z_t.values[:, np.newaxis], lat_t.values)\n",
    "# dx = np.zeros((50, 159, 359))\n",
    "# dy = np.zeros((50, 159, 359))\n",
    "\n",
    "# for i in range(359):\n",
    "#     for j in range(159):\n",
    "#         for k in range(50):\n",
    "#             dx[k, j, i] = gsw.earth.distance([lon_t[i], lon_t[i+1]], [lat_t[j], lat_t[j]], [P[k, j], P[k, j]])\n",
    "#             dy[k, j, i] = gsw.earth.distance([lon_t[i], lon_t[i]], [lat_t[j], lat_t[j+1]], [P[k, j], P[k, j+1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# np.savez('dx_dy_OCCA',\n",
    "#         dx=dx, dy=dy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 20263.40133391  20263.3695282   20263.33772249  20263.30591678\n",
      "  20263.27411108  20263.24230537  20263.21049966  20263.17867803\n",
      "  20263.14680872  20263.11478037  20263.08229085  20263.04870401\n",
      "  20263.01282717  20262.97278382  20262.92593397  20262.86890636\n",
      "  20262.79786829  20262.7088918   20262.59833517  20262.46328816\n",
      "  20262.30187418  20262.11342536  20261.89860957  20261.65930335\n",
      "  20261.39841695  20261.11959227  20260.82675689  20260.52374383\n",
      "  20260.21390878  20259.89982741  20259.58294711  20259.2627589\n",
      "  20258.93533559  20258.59200864  20258.21962793  20257.80310049\n",
      "  20257.32914728  20256.78881596  20256.17754145  20255.4936395\n",
      "  20254.73666362  20253.90653462  20253.00325248  20252.02681721\n",
      "  20250.97722881  20249.85448728  20248.65859262  20247.38954483\n",
      "  20246.04734391  20244.63198986]\n"
     ]
    }
   ],
   "source": [
    "dxdy = np.load('dx_dy_OCCA.npz')\n",
    "dx = dxdy['dx']\n",
    "dy = dxdy['dy']\n",
    "\n",
    "print dx[:, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dx_full = np.zeros((50, 160, 359))\n",
    "dy_full = np.zeros((50, 159, 360))\n",
    "dx_full[:, :-1] = dx\n",
    "dy_full[:, :, :-1] = dy\n",
    "\n",
    "for k in range(50):\n",
    "    dx_full[k, -1] = gsw.earth.distance([lon_t[-2], lon_t[-1]], [lat_t[-1], lat_t[-1]], [P[k, -1], P[k, -1]])\n",
    "    dy_full[k, :, -1] = gsw.earth.distance([lon_t[-1], lon_t[-1]], [lat_t[-2], lat_t[-1]], [P[k, -2], P[k, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 160, 360)\n",
      "(50, 160, 359)\n"
     ]
    }
   ],
   "source": [
    "print potP.shape\n",
    "print (-potP + np.roll(potP, -1, axis=2))[:, :, :-1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dPdx = (-potP + np.roll(potP, -1, axis=2))[:, :, :-1]/dx_full\n",
    "dPdy = (-potP + np.roll(potP, -1, axis=1))[:, :-1]/dy_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Depth_c' (Depth_c: 50)>\n",
      "array([  5.00000000e+00,   1.50000000e+01,   2.50000000e+01,\n",
      "         3.50000000e+01,   4.50000000e+01,   5.50000000e+01,\n",
      "         6.50000000e+01,   7.50050049e+01,   8.50250015e+01,\n",
      "         9.50950012e+01,   1.05309998e+02,   1.15870003e+02,\n",
      "         1.27150002e+02,   1.39739990e+02,   1.54470001e+02,\n",
      "         1.72399994e+02,   1.94735001e+02,   2.22710007e+02,\n",
      "         2.57470001e+02,   2.99929993e+02,   3.50679993e+02,\n",
      "         4.09929993e+02,   4.77470001e+02,   5.52710022e+02,\n",
      "         6.34735046e+02,   7.22400024e+02,   8.14470093e+02,\n",
      "         9.09740112e+02,   1.00715503e+03,   1.10590503e+03,\n",
      "         1.20553503e+03,   1.30620508e+03,   1.40914990e+03,\n",
      "         1.51709497e+03,   1.63417480e+03,   1.76513477e+03,\n",
      "         1.91414990e+03,   2.08403491e+03,   2.27622510e+03,\n",
      "         2.49125000e+03,   2.72925000e+03,   2.99025000e+03,\n",
      "         3.27425000e+03,   3.58125000e+03,   3.91125000e+03,\n",
      "         4.26425000e+03,   4.64025000e+03,   5.03925000e+03,\n",
      "         5.46125000e+03,   5.90625000e+03], dtype=float32)\n",
      "Coordinates:\n",
      "  * Depth_c  (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "Attributes:\n",
      "    long_name: Depth of T grid points                                                  \n",
      "    standard_name: depth\n",
      "    units: m               \n",
      "    positive: down\n",
      "    point_spacing: uneven\n",
      "    edges: Depth_w <xarray.DataArray 'Depth_u' (Depth_u: 50)>\n",
      "array([    0.        ,    10.        ,    20.        ,    30.        ,\n",
      "          40.        ,    50.        ,    60.        ,    70.        ,\n",
      "          80.01000214,    90.04000092,   100.15000153,   110.47000122,\n",
      "         121.27000427,   133.02999878,   146.44999695,   162.48999023,\n",
      "         182.30999756,   207.16000366,   238.26000977,   276.67999268,\n",
      "         323.17999268,   378.17999268,   441.67999268,   513.26000977,\n",
      "         592.16003418,   677.31005859,   767.49005127,   861.45007324,\n",
      "         958.03009033,  1056.2800293 ,  1155.5300293 ,  1255.54003906,\n",
      "        1356.86999512,  1461.42993164,  1572.7598877 ,  1695.58984375,\n",
      "        1834.67980957,  1993.61987305,  2174.44995117,  2378.        ,\n",
      "        2604.5       ,  2854.        ,  3126.5       ,  3422.        ,\n",
      "        3740.5       ,  4082.        ,  4446.5       ,  4834.        ,\n",
      "        5244.5       ,  5678.        ], dtype=float32)\n",
      "Coordinates:\n",
      "  * Depth_u  (Depth_u) float32 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.01 ...\n",
      "Attributes:\n",
      "    long_name: Depth at top of T box                                                   \n",
      "    units: m               \n",
      "    positive: down\n",
      "    point_spacing: uneven\n"
     ]
    }
   ],
   "source": [
    "print z_t, z_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Latitude_t' (Latitude_t: 160)>\n",
      "array([-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5,\n",
      "       -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5,\n",
      "       -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5,\n",
      "       -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5,\n",
      "       -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5,\n",
      "       -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5,\n",
      "       -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5,\n",
      "       -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,\n",
      "        -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,\n",
      "         1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
      "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,\n",
      "        19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,\n",
      "        28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,\n",
      "        37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,\n",
      "        46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,\n",
      "        55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,  63.5,\n",
      "        64.5,  65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,  72.5,\n",
      "        73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * Latitude_t  (Latitude_t) float32 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 ...\n",
      "Attributes:\n",
      "    long_name: Latitude on T grid                                                      \n",
      "    standard_name: latitude\n",
      "    units: degree_north     <xarray.DataArray 'Latitude_v' (Latitude_v: 160)>\n",
      "array([-80., -79., -78., -77., -76., -75., -74., -73., -72., -71., -70.,\n",
      "       -69., -68., -67., -66., -65., -64., -63., -62., -61., -60., -59.,\n",
      "       -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
      "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37.,\n",
      "       -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26.,\n",
      "       -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15.,\n",
      "       -14., -13., -12., -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,\n",
      "        -3.,  -2.,  -1.,   0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,\n",
      "         8.,   9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,\n",
      "        19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,\n",
      "        30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,\n",
      "        41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,\n",
      "        52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,\n",
      "        63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,\n",
      "        74.,  75.,  76.,  77.,  78.,  79.], dtype=float32)\n",
      "Coordinates:\n",
      "  * Latitude_v  (Latitude_v) float32 -80.0 -79.0 -78.0 -77.0 -76.0 -75.0 ...\n",
      "Attributes:\n",
      "    long_name: Latitude on V grid                                                      \n",
      "    units: degree          \n",
      "<xarray.DataArray 'Longitude_t' (Longitude_t: 360)>\n",
      "array([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,\n",
      "          8.5,    9.5,   10.5,   11.5,   12.5,   13.5,   14.5,   15.5,\n",
      "         16.5,   17.5,   18.5,   19.5,   20.5,   21.5,   22.5,   23.5,\n",
      "         24.5,   25.5,   26.5,   27.5,   28.5,   29.5,   30.5,   31.5,\n",
      "         32.5,   33.5,   34.5,   35.5,   36.5,   37.5,   38.5,   39.5,\n",
      "         40.5,   41.5,   42.5,   43.5,   44.5,   45.5,   46.5,   47.5,\n",
      "         48.5,   49.5,   50.5,   51.5,   52.5,   53.5,   54.5,   55.5,\n",
      "         56.5,   57.5,   58.5,   59.5,   60.5,   61.5,   62.5,   63.5,\n",
      "         64.5,   65.5,   66.5,   67.5,   68.5,   69.5,   70.5,   71.5,\n",
      "         72.5,   73.5,   74.5,   75.5,   76.5,   77.5,   78.5,   79.5,\n",
      "         80.5,   81.5,   82.5,   83.5,   84.5,   85.5,   86.5,   87.5,\n",
      "         88.5,   89.5,   90.5,   91.5,   92.5,   93.5,   94.5,   95.5,\n",
      "         96.5,   97.5,   98.5,   99.5,  100.5,  101.5,  102.5,  103.5,\n",
      "        104.5,  105.5,  106.5,  107.5,  108.5,  109.5,  110.5,  111.5,\n",
      "        112.5,  113.5,  114.5,  115.5,  116.5,  117.5,  118.5,  119.5,\n",
      "        120.5,  121.5,  122.5,  123.5,  124.5,  125.5,  126.5,  127.5,\n",
      "        128.5,  129.5,  130.5,  131.5,  132.5,  133.5,  134.5,  135.5,\n",
      "        136.5,  137.5,  138.5,  139.5,  140.5,  141.5,  142.5,  143.5,\n",
      "        144.5,  145.5,  146.5,  147.5,  148.5,  149.5,  150.5,  151.5,\n",
      "        152.5,  153.5,  154.5,  155.5,  156.5,  157.5,  158.5,  159.5,\n",
      "        160.5,  161.5,  162.5,  163.5,  164.5,  165.5,  166.5,  167.5,\n",
      "        168.5,  169.5,  170.5,  171.5,  172.5,  173.5,  174.5,  175.5,\n",
      "        176.5,  177.5,  178.5,  179.5,  180.5,  181.5,  182.5,  183.5,\n",
      "        184.5,  185.5,  186.5,  187.5,  188.5,  189.5,  190.5,  191.5,\n",
      "        192.5,  193.5,  194.5,  195.5,  196.5,  197.5,  198.5,  199.5,\n",
      "        200.5,  201.5,  202.5,  203.5,  204.5,  205.5,  206.5,  207.5,\n",
      "        208.5,  209.5,  210.5,  211.5,  212.5,  213.5,  214.5,  215.5,\n",
      "        216.5,  217.5,  218.5,  219.5,  220.5,  221.5,  222.5,  223.5,\n",
      "        224.5,  225.5,  226.5,  227.5,  228.5,  229.5,  230.5,  231.5,\n",
      "        232.5,  233.5,  234.5,  235.5,  236.5,  237.5,  238.5,  239.5,\n",
      "        240.5,  241.5,  242.5,  243.5,  244.5,  245.5,  246.5,  247.5,\n",
      "        248.5,  249.5,  250.5,  251.5,  252.5,  253.5,  254.5,  255.5,\n",
      "        256.5,  257.5,  258.5,  259.5,  260.5,  261.5,  262.5,  263.5,\n",
      "        264.5,  265.5,  266.5,  267.5,  268.5,  269.5,  270.5,  271.5,\n",
      "        272.5,  273.5,  274.5,  275.5,  276.5,  277.5,  278.5,  279.5,\n",
      "        280.5,  281.5,  282.5,  283.5,  284.5,  285.5,  286.5,  287.5,\n",
      "        288.5,  289.5,  290.5,  291.5,  292.5,  293.5,  294.5,  295.5,\n",
      "        296.5,  297.5,  298.5,  299.5,  300.5,  301.5,  302.5,  303.5,\n",
      "        304.5,  305.5,  306.5,  307.5,  308.5,  309.5,  310.5,  311.5,\n",
      "        312.5,  313.5,  314.5,  315.5,  316.5,  317.5,  318.5,  319.5,\n",
      "        320.5,  321.5,  322.5,  323.5,  324.5,  325.5,  326.5,  327.5,\n",
      "        328.5,  329.5,  330.5,  331.5,  332.5,  333.5,  334.5,  335.5,\n",
      "        336.5,  337.5,  338.5,  339.5,  340.5,  341.5,  342.5,  343.5,\n",
      "        344.5,  345.5,  346.5,  347.5,  348.5,  349.5,  350.5,  351.5,\n",
      "        352.5,  353.5,  354.5,  355.5,  356.5,  357.5,  358.5,  359.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * Longitude_t  (Longitude_t) float32 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 ...\n",
      "Attributes:\n",
      "    long_name: Longitude on T grid                                                     \n",
      "    standard_name: longitude\n",
      "    units: degree_east     \n",
      "    point_spacing: even\n",
      "    modulo:   <xarray.DataArray 'Longitude_u' (Longitude_u: 360)>\n",
      "array([   0.,    1.,    2.,    3.,    4.,    5.,    6.,    7.,    8.,\n",
      "          9.,   10.,   11.,   12.,   13.,   14.,   15.,   16.,   17.,\n",
      "         18.,   19.,   20.,   21.,   22.,   23.,   24.,   25.,   26.,\n",
      "         27.,   28.,   29.,   30.,   31.,   32.,   33.,   34.,   35.,\n",
      "         36.,   37.,   38.,   39.,   40.,   41.,   42.,   43.,   44.,\n",
      "         45.,   46.,   47.,   48.,   49.,   50.,   51.,   52.,   53.,\n",
      "         54.,   55.,   56.,   57.,   58.,   59.,   60.,   61.,   62.,\n",
      "         63.,   64.,   65.,   66.,   67.,   68.,   69.,   70.,   71.,\n",
      "         72.,   73.,   74.,   75.,   76.,   77.,   78.,   79.,   80.,\n",
      "         81.,   82.,   83.,   84.,   85.,   86.,   87.,   88.,   89.,\n",
      "         90.,   91.,   92.,   93.,   94.,   95.,   96.,   97.,   98.,\n",
      "         99.,  100.,  101.,  102.,  103.,  104.,  105.,  106.,  107.,\n",
      "        108.,  109.,  110.,  111.,  112.,  113.,  114.,  115.,  116.,\n",
      "        117.,  118.,  119.,  120.,  121.,  122.,  123.,  124.,  125.,\n",
      "        126.,  127.,  128.,  129.,  130.,  131.,  132.,  133.,  134.,\n",
      "        135.,  136.,  137.,  138.,  139.,  140.,  141.,  142.,  143.,\n",
      "        144.,  145.,  146.,  147.,  148.,  149.,  150.,  151.,  152.,\n",
      "        153.,  154.,  155.,  156.,  157.,  158.,  159.,  160.,  161.,\n",
      "        162.,  163.,  164.,  165.,  166.,  167.,  168.,  169.,  170.,\n",
      "        171.,  172.,  173.,  174.,  175.,  176.,  177.,  178.,  179.,\n",
      "        180.,  181.,  182.,  183.,  184.,  185.,  186.,  187.,  188.,\n",
      "        189.,  190.,  191.,  192.,  193.,  194.,  195.,  196.,  197.,\n",
      "        198.,  199.,  200.,  201.,  202.,  203.,  204.,  205.,  206.,\n",
      "        207.,  208.,  209.,  210.,  211.,  212.,  213.,  214.,  215.,\n",
      "        216.,  217.,  218.,  219.,  220.,  221.,  222.,  223.,  224.,\n",
      "        225.,  226.,  227.,  228.,  229.,  230.,  231.,  232.,  233.,\n",
      "        234.,  235.,  236.,  237.,  238.,  239.,  240.,  241.,  242.,\n",
      "        243.,  244.,  245.,  246.,  247.,  248.,  249.,  250.,  251.,\n",
      "        252.,  253.,  254.,  255.,  256.,  257.,  258.,  259.,  260.,\n",
      "        261.,  262.,  263.,  264.,  265.,  266.,  267.,  268.,  269.,\n",
      "        270.,  271.,  272.,  273.,  274.,  275.,  276.,  277.,  278.,\n",
      "        279.,  280.,  281.,  282.,  283.,  284.,  285.,  286.,  287.,\n",
      "        288.,  289.,  290.,  291.,  292.,  293.,  294.,  295.,  296.,\n",
      "        297.,  298.,  299.,  300.,  301.,  302.,  303.,  304.,  305.,\n",
      "        306.,  307.,  308.,  309.,  310.,  311.,  312.,  313.,  314.,\n",
      "        315.,  316.,  317.,  318.,  319.,  320.,  321.,  322.,  323.,\n",
      "        324.,  325.,  326.,  327.,  328.,  329.,  330.,  331.,  332.,\n",
      "        333.,  334.,  335.,  336.,  337.,  338.,  339.,  340.,  341.,\n",
      "        342.,  343.,  344.,  345.,  346.,  347.,  348.,  349.,  350.,\n",
      "        351.,  352.,  353.,  354.,  355.,  356.,  357.,  358.,  359.], dtype=float32)\n",
      "Coordinates:\n",
      "  * Longitude_u  (Longitude_u) float32 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 ...\n",
      "Attributes:\n",
      "    long_name: Longitude on U grid                                                     \n",
      "    units: degree          \n",
      "    modulo: 360\n"
     ]
    }
   ],
   "source": [
    "print lat_t, lat_v\n",
    "print lon_t, lon_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 360) (50, 160, 360) (50,) (160,) (360,)\n",
      "(50, 160, 360)\n"
     ]
    }
   ],
   "source": [
    "print eta_s.shape, sal.shape, z_t.shape, lat_t.shape, lon_t.shape\n",
    "print v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "p_t = gsw.p_from_z(-z_t.values[:, np.newaxis], lat_t.values[np.newaxis, :])\n",
    "absS = gsw.SA_from_SP(sal.values, p_t[:, :, np.newaxis], \n",
    "                      lon_t.values[np.newaxis, np.newaxis, :], lat_t.values[np.newaxis, :, np.newaxis])\n",
    "consT = gsw.CT_from_pt(absS, theta.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 160, 360) (50, 160, 360)\n"
     ]
    }
   ],
   "source": [
    "print absS.shape, consT.shape\n",
    "# print p_t, absS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "potrho = gsw.rho(absS, consT, 0.)\n",
    "N2, p_N2 = gsw.Nsquared(absS, consT, \n",
    "                        p_t[:, :, np.newaxis], lat_t.values[np.newaxis, :, np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z_N2 = gsw.z_from_p(p_N2, lat_t.values[np.newaxis, :, np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f0 = gsw.earth.f(lat_t.values)\n",
    "beta = 2.*gsw.earth.OMEGA/gsw.earth.earth_radius * np.cos(np.pi/180.*lat_t.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fy = .5 * (f0 + np.roll(f0, -1, axis=0))[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $u_g = - \\frac{1}{f_0} \\frac{\\partial \\phi}{\\partial y}, \\ \\ v_g = \\frac{1}{f_0} \\frac{\\partial \\phi}{\\partial x}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fbe98329650>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rDpGUeqCc4XQ09I5mbbiwoXd+1fmVvXj9x50Y+nzlzEYkIGwzdJbcKf3MkVaO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\nLVq8avH/A7cwyrhI/EWJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe99daa390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ug = - dPdy / fy[np.newaxis, :, np.newaxis]\n",
    "Vg = dPdx / f0[np.newaxis, :, np.newaxis]\n",
    "\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax1 = ax1.pcolormesh(np.ma.masked_invalid(Ug[0]), \n",
    "                      vmin=-.2, vmax=.2, cmap='RdBu_r')\n",
    "fig.colorbar(cax1)\n",
    "ax2 = fig.add_subplot(122)\n",
    "cax2 = ax2.pcolormesh(np.ma.masked_invalid(Vg[0]), \n",
    "                      vmin=-.2, vmax=.2, cmap='RdBu_r')\n",
    "fig.colorbar(cax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "maskUg = np.isnan(Ug.values)\n",
    "maskVg = np.isnan(Vg.values)\n",
    "maskT = np.isnan(theta.values)\n",
    "\n",
    "Ug.values[maskUg] = 0.\n",
    "Vg.values[maskVg] = 0.\n",
    "\n",
    "Vg_coinT = xray.DataArray(.5 * (Vg + np.roll(Vg, -1, axis=2))[:, :, :-1], \n",
    "                          coords=[('Depth_c', z_u.coords['Depth_u']), \n",
    "                                     ('Latitude_t',theta.coords['Latitude_t']), ('Longitude_Vg', theta.coords['Longitude_t'][1:-1])], \n",
    "                         dims=['Depth_c', 'Latitude_t', 'Longitude_Vg']).where(~maskT[:, :, 1:-1])\n",
    "Ug_coinT = xray.DataArray(.5 * (Ug + np.roll(Ug, -1, axis=1))[:, :-1], coords=[('Depth_c', z_u.coords['Depth_u']), \n",
    "                                     ('Latitude_Ug',theta.coords['Latitude_t'][1:-1]), ('Longitude_t', theta.coords['Longitude_t'])], \n",
    "                         dims=['Depth_c', 'Latitude_Ug', 'Longitude_t']).where(~maskT[:, 1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "absS_meta = xray.DataArray(absS, coords=theta.coords, dims=theta.dims)\n",
    "consT_meta = xray.DataArray(consT, coords=theta.coords, dims=theta.dims)\n",
    "potrho_meta = xray.DataArray(potrho, coords=theta.coords, dims=theta.dims)\n",
    "f0_meta = xray.DataArray(f0, coords=lat_t.coords, dims=lat_t.dims)\n",
    "beta_meta = xray.DataArray(beta, coords=lat_t.coords, dims=lat_t.dims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N2_meta = xray.DataArray(N2, coords=[('Depth_N2', z_u.coords['Depth_u'][1:]), \n",
    "                                     ('Latitude_t',theta.coords['Latitude_t']), ('Longitude_t', theta.coords['Longitude_t'])], \n",
    "                         dims=['Depth_N2', 'Latitude_t', 'Longitude_t'])\n",
    "zN2_meta = xray.DataArray(z_N2, coords=[('Depth_N2', z_u.coords['Depth_u'][1:]), \n",
    "                                     ('Latitude_t',theta.coords['Latitude_t']), ('Longitude_t', theta.coords['Longitude_t'])], \n",
    "                         dims=['Depth_N2', 'Latitude_t', 'Longitude_t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360,) (u'Depth_c', u'Latitude_t', u'Longitude_t') (49, 160, 360)\n",
      "Coordinates:\n",
      "  * Depth_u  (Depth_u) float32 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.01 ... (u'Depth_u',)\n"
     ]
    }
   ],
   "source": [
    "print theta.coords['Longitude_t'].shape, theta.dims, N2.shape\n",
    "print z_u.coords, z_u.dims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rigid lid & flat ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Longitude_u' (Longitude_u: 20)>\n",
      "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
      "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.], dtype=float32)\n",
      "Coordinates:\n",
      "  * Longitude_u  (Longitude_u) float32 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 ...\n",
      "Attributes:\n",
      "    long_name: Longitude on U grid                                                     \n",
      "    units: degree          \n",
      "    modulo: 360 <xarray.DataArray 'Longitude_t' (Longitude_t: 20)>\n",
      "array([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,\n",
      "         9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,\n",
      "        18.5,  19.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * Longitude_t  (Longitude_t) float32 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 ...\n",
      "Attributes:\n",
      "    long_name: Longitude on T grid                                                     \n",
      "    standard_name: longitude\n",
      "    units: degree_east     \n",
      "    point_spacing: even\n",
      "    modulo:  \n",
      "<xarray.DataArray 'Latitude_v' (Latitude_v: 20)>\n",
      "array([-80., -79., -78., -77., -76., -75., -74., -73., -72., -71., -70.,\n",
      "       -69., -68., -67., -66., -65., -64., -63., -62., -61.], dtype=float32)\n",
      "Coordinates:\n",
      "  * Latitude_v  (Latitude_v) float32 -80.0 -79.0 -78.0 -77.0 -76.0 -75.0 ...\n",
      "Attributes:\n",
      "    long_name: Latitude on V grid                                                      \n",
      "    units: degree           <xarray.DataArray 'Latitude_t' (Latitude_t: 20)>\n",
      "array([-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5,\n",
      "       -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5,\n",
      "       -61.5, -60.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * Latitude_t  (Latitude_t) float32 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 ...\n",
      "Attributes:\n",
      "    long_name: Latitude on T grid                                                      \n",
      "    standard_name: latitude\n",
      "    units: degree_north    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f57b6053dd0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5APAFoATerKpfBmg7dnNXc+Y4J1KZLJ90aqL6FYf7YmueF1u5+ujQ4VzCmSRNbGogzTIK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sX61mcTVVk9DpCX32hDfusO0HQLNNlGFfKCadxI5Oan8+gOLCvZlKIbgGQw3r4Yg8y4cNlUp4ao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\nzKySX9bT+pedzCIhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57b5e3f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print lon_u[:20], lon_t[:20]\n",
    "print lat_v[:20], lat_t[:20]\n",
    "xray.plot.imshow((.5*(u.roll(Longitude_u=-1) + u))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "maskU = np.isnan(u.values)\n",
    "maskV = np.isnan(v.values)\n",
    "\n",
    "u.values[maskU] = 0.\n",
    "v.values[maskV] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'u' (Depth_c: 50, Latitude_t: 100, Longitude_u: 360)>\n",
      "array([[[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       ..., \n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]]])\n",
      "Coordinates:\n",
      "  * Longitude_u  (Longitude_u) float32 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 ...\n",
      "  * Latitude_t   (Latitude_t) float32 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 ...\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    long_name: Zonal Velocity                                                          \n",
      "    units: m/s             \n",
      "    grid: UM <xarray.DataArray 'v' (Depth_c: 50, Latitude_v: 100, Longitude_t: 360)>\n",
      "array([[[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       ..., \n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        ..., \n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]]])\n",
      "Coordinates:\n",
      "  * Longitude_t  (Longitude_t) float32 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 ...\n",
      "  * Latitude_v   (Latitude_v) float32 -80.0 -79.0 -78.0 -77.0 -76.0 -75.0 ...\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    long_name: Meridional Velocity                                                     \n",
      "    units: m/s             \n",
      "    grid: VM\n"
     ]
    }
   ],
   "source": [
    "print u[:100, :100], v[:100, :100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "u_coinT = xray.DataArray((.5*(u.roll(Longitude_u=-1).values + u.values)),\n",
    "                      coords=theta.coords, dims=theta.dims).where(~maskT)\n",
    "v_coinT = xray.DataArray((.5*(v.roll(Latitude_v=-1).values + v.values)),\n",
    "                      coords=theta.coords, dims=theta.dims).where(~maskT)\n",
    "# print u, u_coinT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gulf Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'u' (Depth_c: 50)>\n",
      "array([ 0.23944686,  0.202685  ,  0.18200348,  0.17409228,  0.16885349,\n",
      "        0.16435893,  0.16032954,  0.15664397,  0.15308236,  0.14954343,\n",
      "        0.14679012,  0.14432698,  0.1414368 ,  0.13848211,  0.13460122,\n",
      "        0.13018939,  0.1244467 ,  0.11745287,  0.10890713,  0.09883665,\n",
      "        0.08717503,  0.07444824,  0.06123254,  0.04839063,  0.03677421,\n",
      "        0.0268672 ,  0.01867473,  0.0121845 ,  0.00760518,  0.00489767,\n",
      "        0.00353413,  0.00285542,  0.0024165 ,  0.00199221,  0.00144401,\n",
      "        0.0006767 , -0.00030298, -0.00136913, -0.00236471, -0.00318346,\n",
      "       -0.00376874, -0.00404952, -0.00395442, -0.00351102, -0.00283759,\n",
      "       -0.00198498, -0.00068925,  0.00059345,  0.        ,  0.        ])\n",
      "Coordinates:\n",
      "    Longitude_u  float32 299.0\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    long_name: Zonal Velocity                                                          \n",
      "    units: m/s             \n",
      "    grid: UM <xarray.DataArray 'u' (Depth_c: 50)>\n",
      "array([ 0.24281602,  0.20863804,  0.18932983,  0.18118398,  0.17612447,\n",
      "        0.17162971,  0.16771592,  0.16384149,  0.16035922,  0.15705316,\n",
      "        0.1543837 ,  0.15169521,  0.14882469,  0.14594722,  0.14190947,\n",
      "        0.13762632,  0.13213648,  0.12526578,  0.1166122 ,  0.10635603,\n",
      "        0.09433772,  0.08094706,  0.06682488,  0.05285184,  0.04002678,\n",
      "        0.02895699,  0.01978298,  0.01256612,  0.00750119,  0.004512  ,\n",
      "        0.00304223,  0.0023826 ,  0.00201441,  0.00166243,  0.0011796 ,\n",
      "        0.00048553, -0.00039988, -0.00135567, -0.00224543, -0.00298431,\n",
      "       -0.00353272, -0.00382951, -0.00377605, -0.00336971, -0.00271207,\n",
      "       -0.00179736, -0.00051487,  0.0006103 ,  0.        ,  0.        ])\n",
      "Coordinates:\n",
      "    Longitude_u  float32 300.0\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    long_name: Zonal Velocity                                                          \n",
      "    units: m/s             \n",
      "    grid: UM\n",
      "<xarray.DataArray 'v' (Depth_c: 50)>\n",
      "array([ 0.03967041,  0.03510151,  0.04019022,  0.04668061,  0.04913285,\n",
      "        0.0502381 ,  0.05102414,  0.0515379 ,  0.05193662,  0.05222316,\n",
      "        0.05213001,  0.05184483,  0.05137736,  0.05068032,  0.04974374,\n",
      "        0.04858968,  0.04708494,  0.04507946,  0.04265563,  0.03937409,\n",
      "        0.03542616,  0.03087777,  0.02564656,  0.02021027,  0.0150082 ,\n",
      "        0.01028549,  0.00620139,  0.00287003,  0.00046666, -0.00091146,\n",
      "       -0.00144855, -0.00152616, -0.00147558, -0.00145375, -0.00149181,\n",
      "       -0.00157445, -0.00168814, -0.0018392 , -0.00204265, -0.00229426,\n",
      "       -0.00256875, -0.0028379 , -0.00305751, -0.0031456 , -0.00304893,\n",
      "       -0.00274524, -0.00244838,  0.        ,  0.        ,  0.        ])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_v   float32 39.0\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    long_name: Meridional Velocity                                                     \n",
      "    units: m/s             \n",
      "    grid: VM <xarray.DataArray 'v' (Depth_c: 50)>\n",
      "array([ 0.03498516,  0.03102324,  0.03670016,  0.04119537,  0.04248637,\n",
      "        0.04322845,  0.0436029 ,  0.04383555,  0.0438052 ,  0.043598  ,\n",
      "        0.04321078,  0.04266092,  0.04194829,  0.04106502,  0.03984407,\n",
      "        0.03842646,  0.03672103,  0.03459246,  0.03208185,  0.02906089,\n",
      "        0.02571714,  0.02202296,  0.01798431,  0.01410491,  0.01057055,\n",
      "        0.00744473,  0.00468729,  0.00233127,  0.00059059, -0.00039309,\n",
      "       -0.00077711, -0.00087736, -0.00092168, -0.00099712, -0.0011234 ,\n",
      "       -0.00130368, -0.00154013, -0.00183272, -0.00217052, -0.00252132,\n",
      "       -0.00283725, -0.00308227, -0.00323668, -0.00326342, -0.00314228,\n",
      "       -0.00293316, -0.00262752,  0.        ,  0.        ,  0.        ])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_v   float32 40.0\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    long_name: Meridional Velocity                                                     \n",
      "    units: m/s             \n",
      "    grid: VM\n"
     ]
    }
   ],
   "source": [
    "print u.sel(Longitude_u=GS[0]-.5, Latitude_t=GS[1]), u.sel(Longitude_u=GS[0]+.5, Latitude_t=GS[1])\n",
    "print v.sel(Longitude_t=GS[0], Latitude_v=GS[1]-.5), v.sel(Longitude_t=GS[0], Latitude_v=GS[1]+.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "GS = np.array([299.5, 39.5])\n",
    "u_GS = Ug_coinT.sel(Longitude_t=GS[0], Latitude_Ug=GS[1])\n",
    "v_GS = Vg_coinT.sel(Longitude_Vg=GS[0], Latitude_t=GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "absS_GS = absS_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "consT_GS = consT_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "rho_GS = rho_anom.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "potrho_GS = potrho_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N2_GS = N2_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "zN2_GS = zN2_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (Depth_c: 50)>\n",
      "array([ 36.37705617,  36.38829268,  36.43808331,  36.49948204,\n",
      "        36.53953381,  36.55776704,  36.56630143,  36.57135183,\n",
      "        36.57744907,  36.57823244,  36.57765099,  36.57286845,\n",
      "        36.56452534,  36.55226472,  36.53229802,  36.50537004,\n",
      "        36.46777153,  36.41438766,  36.34106086,  36.24866002,\n",
      "        36.1329151 ,  35.98797394,  35.82308989,  35.66429781,\n",
      "        35.51827826,  35.39446247,  35.29708428,  35.22529779,\n",
      "        35.17724572,  35.15076161,  35.14115192,  35.14007741,\n",
      "        35.13925532,  35.13451384,  35.12538326,  35.11673309,\n",
      "        35.11542681,  35.12178446,  35.12816684,  35.12755937,\n",
      "        35.12004502,  35.11000069,  35.10029747,  35.09019658,\n",
      "        35.0800635 ,  35.07483442,  35.07510403,  35.07778868,\n",
      "                nan,          nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray (Depth_c: 50)>\n",
      "array([ 21.10233502,  20.96159023,  20.58858233,  20.10892434,\n",
      "        19.67015963,  19.28889264,  18.9548632 ,  18.66008012,\n",
      "        18.39584352,  18.14253007,  17.92917217,  17.72578309,\n",
      "        17.5173952 ,  17.30134111,  17.04102643,  16.74390922,\n",
      "        16.38022699,  15.91659694,  15.32224204,  14.60786933,\n",
      "        13.74052471,  12.67512429,  11.44249457,  10.1875584 ,\n",
      "         8.96519207,   7.83072439,   6.78052544,   5.812746  ,\n",
      "         5.0236021 ,   4.50855976,   4.24120976,   4.10832433,\n",
      "         4.01202286,   3.90748005,   3.77673659,   3.63052264,\n",
      "         3.49743043,   3.38338822,   3.25466227,   3.0790568 ,\n",
      "         2.85941375,   2.62189665,   2.38680162,   2.16975342,\n",
      "         2.0088036 ,   1.92156767,   1.8580864 ,   1.81438341,\n",
      "                nan,          nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray (Depth_c: 49)>\n",
      "array([  4.49013733e-05,   1.32619531e-04,   1.67213349e-04,\n",
      "         1.39980381e-04,   1.08620176e-04,   8.89353316e-05,\n",
      "         7.59696940e-05,   6.85660114e-05,   6.13053056e-05,\n",
      "         4.96623307e-05,   4.26153730e-05,   3.83933836e-05,\n",
      "         3.33244718e-05,   3.14573673e-05,   2.74780120e-05,\n",
      "         2.50869570e-05,   2.35907064e-05,   2.26047540e-05,\n",
      "         2.06309696e-05,   1.93499569e-05,   1.85802260e-05,\n",
      "         1.73887368e-05,   1.49995125e-05,   1.25945779e-05,\n",
      "         1.06210281e-05,   9.69791903e-06,   9.18845407e-06,\n",
      "         7.56655382e-06,   5.00383224e-06,   2.83633215e-06,\n",
      "         1.67170878e-06,   1.19350982e-06,   9.87252978e-07,\n",
      "         9.54096555e-07,   1.05979705e-06,   1.18957478e-06,\n",
      "         1.23432375e-06,   1.22190029e-06,   1.20084648e-06,\n",
      "         1.18287498e-06,   1.14801849e-06,   1.08643614e-06,\n",
      "         9.36511867e-07,   6.17342678e-07,   3.35507108e-07,\n",
      "         3.19646379e-07,   2.60733349e-07,              nan,\n",
      "                    nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "<xarray.DataArray (Depth_c: 50)>\n",
      "array([ 0.24113144,  0.20566152,  0.18566666,  0.17763813,  0.17248898,\n",
      "        0.16799432,  0.16402273,  0.16024273,  0.15672079,  0.1532983 ,\n",
      "        0.15058691,  0.1480111 ,  0.14513075,  0.14221466,  0.13825534,\n",
      "        0.13390785,  0.12829159,  0.12135932,  0.11275966,  0.10259634,\n",
      "        0.09075638,  0.07769765,  0.06402871,  0.05062123,  0.0384005 ,\n",
      "        0.02791209,  0.01922886,  0.01237531,  0.00755318,  0.00470483,\n",
      "        0.00328818,  0.00261901,  0.00221545,  0.00182732,  0.0013118 ,\n",
      "        0.00058111, -0.00035143, -0.0013624 , -0.00230507, -0.00308389,\n",
      "       -0.00365073, -0.00393951, -0.00386523, -0.00344036, -0.00277483,\n",
      "       -0.00189117, -0.00060206,  0.00060187,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray (Depth_c: 50)>\n",
      "array([ 0.03732778,  0.03306238,  0.03844519,  0.04393799,  0.04580961,\n",
      "        0.04673327,  0.04731352,  0.04768672,  0.04787091,  0.04791058,\n",
      "        0.0476704 ,  0.04725287,  0.04666282,  0.04587267,  0.04479391,\n",
      "        0.04350807,  0.04190299,  0.03983596,  0.03736874,  0.03421749,\n",
      "        0.03057165,  0.02645037,  0.02181544,  0.01715759,  0.01278938,\n",
      "        0.00886511,  0.00544434,  0.00260065,  0.00052863, -0.00065227,\n",
      "       -0.00111283, -0.00120176, -0.00119863, -0.00122544, -0.00130761,\n",
      "       -0.00143906, -0.00161413, -0.00183596, -0.00210659, -0.00240779,\n",
      "       -0.002703  , -0.00296008, -0.00314709, -0.00320451, -0.00309561,\n",
      "       -0.0028392 , -0.00253795,  0.        ,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n"
     ]
    }
   ],
   "source": [
    "print absS_GS, consT_GS, N2_GS\n",
    "print u_GS, v_GS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_func(x, A, K, C):\n",
    "    return A * np.exp(K * x) + C\n",
    "\n",
    "def fit_exp_linear(x, y, C=0):\n",
    "    \"\"\"fit an exponential\"\"\"\n",
    "    y = y - C\n",
    "    y = np.log(y)\n",
    "    K, A_log = np.polyfit(x, y, 1)\n",
    "    A = np.exp(A_log)\n",
    "    return A, K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "u0 = u_GS.values[-3]; v0 = v_GS.values[-3]; N20 = N2_GS.values[-3]\n",
    "\n",
    "# u_raw = u_GS.values.copy()\n",
    "# v_raw = v_GS.values.copy()\n",
    "# u_raw[u_raw<0.] = 1e-3\n",
    "# v_raw[v_raw<0.] = 1e-3\n",
    "\n",
    "A, K = fit_exp_linear(z_t.values[:-3], u_GS.values[:-3], C=-5e-3)\n",
    "u_fit = model_func(z_t.values, A, K, -5e-3)\n",
    "A, K = fit_exp_linear(z_t.values[:-3], v_GS.values[:-3], C=-5e-3)\n",
    "v_fit = model_func(z_t.values, A, K, -5e-3)\n",
    "A, K = fit_exp_linear(-zN2_GS.values[:-3], N2_GS.values[:-3], C=N20)\n",
    "N2_fit = model_func(-zN2_GS.values, A, K, N20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SksKyZcu4/PLL82crspMnYfZsmD4dvvvOPfeqVcsVU4cOdTMOB3lykZSUFHbu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\nSVKF/f+hikZHhNK2tAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe93f7e590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,5))\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "ax1.plot(potrho_GS.values, -z_t.values, 'k', lw=2)\n",
    "ax1.set_title('Potential density', fontsize=14, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "ax2.plot(u_GS.values, -z_t.values, 'k', lw=2, label=r'$u$')\n",
    "ax2.plot(v_GS.values, -z_t.values, 'k--', lw=2, label=r'$v$')\n",
    "ax2.plot(u_fit, -z_t.values, 'r', lw=2)\n",
    "ax2.plot(v_fit, -z_t.values, 'r--', lw=2)\n",
    "ax2.set_title('Horizontal velocities', fontsize=14, y=1.03)\n",
    "# ax2.set_xlabel('lon', fontsize=12)\n",
    "# ax2.set_ylabel('lat',fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "ax3.plot(N2_GS.values, zN2_GS.values, 'k', lw=2)\n",
    "ax3.plot(N2_fit, zN2_GS.values, 'r', lw=2)\n",
    "ax3.set_title(r'$N^2$', fontsize=16, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zphi_GS, Rd_GS, vd_GS = baroclinic.neutral_modes_from_N2_profile(-zN2_GS.values, \n",
    "                                                        N2_GS.values, f0_meta.sel(Latitude_t=GS[1]).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savez('OCCA_global',\n",
    "        absolute_salinity=absS, conservative_temperature=consT,\n",
    "        potential_density=potrho_meta, \n",
    "        z_N2=zN2_meta, N2=N2_meta,\n",
    "        u_at_Tpoints=u_coinT, v_at_Tpoints=v_coinT,\n",
    "        z_u=z_t\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[            nan  25787.38588424  13900.4970779    9608.06511652\n",
      "   6906.66296569   5876.40199714] (48,) [-0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757] [-0.2287108  -0.22844163 -0.22738221 -0.22538205 -0.22315635 -0.22100572\n",
      " -0.21890111 -0.21681132 -0.21466035 -0.21249153 -0.21051281 -0.20857709\n",
      " -0.20653185 -0.20436013 -0.20172795 -0.19864415 -0.19475028 -0.18960952\n",
      " -0.18268901 -0.17391394 -0.16270843 -0.14845062 -0.13133273 -0.11305419\n",
      " -0.09473064 -0.07691331 -0.05874788 -0.04009015 -0.02387257 -0.01278873\n",
      " -0.00637754 -0.00253038  0.00029387  0.00275014  0.00532013  0.00849032\n",
      "  0.01248929  0.01713441  0.02220176  0.02756655  0.0331077   0.03857259\n",
      "  0.04363333  0.04770119  0.05004272  0.05105026  0.05168191  0.05185931]\n"
     ]
    },
    {
     "data": {
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ZMMebMcV3nxutAzzFtKVl33YorQsRN2/2l6WkwLRpHZIcNgxdb8HrrcPjqd1PSDqKSmvZ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\nIiKRVCxERCTS/wPGrMs1bvfjJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbea00dcf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print Rd_GS, zphi_GS.shape, vd_GS[:, 0], vd_GS[:, 1]\n",
    "plt.figure()\n",
    "for i in range(vd_GS.shape[1]):\n",
    "    plt.plot(vd_GS[:, i], -zphi_GS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savez('OCCA_GulfStream',\n",
    "        absolute_salinity=absS_GS.values, conservative_temperature=consT_GS.values,\n",
    "        potential_density=potrho_GS.values, \n",
    "        z_N2=zN2_GS.values, N2=N2_GS.values,\n",
    "        u_at_Tpoints=u_GS.values, v_at_Tpoints=v_GS.values, z_u=z_t.values,\n",
    "        f0=f0_meta.sel(Latitude_t=GS[1]).values, beta=beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "        Rossby_radii=Rd_GS\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (Depth_c: 49)>\n",
      "array([  4.49013733e-05,   1.32619531e-04,   1.67213349e-04,\n",
      "         1.39980381e-04,   1.08620176e-04,   8.89353316e-05,\n",
      "         7.59696940e-05,   6.85660114e-05,   6.13053056e-05,\n",
      "         4.96623307e-05,   4.26153730e-05,   3.83933836e-05,\n",
      "         3.33244718e-05,   3.14573673e-05,   2.74780120e-05,\n",
      "         2.50869570e-05,   2.35907064e-05,   2.26047540e-05,\n",
      "         2.06309696e-05,   1.93499569e-05,   1.85802260e-05,\n",
      "         1.73887368e-05,   1.49995125e-05,   1.25945779e-05,\n",
      "         1.06210281e-05,   9.69791903e-06,   9.18845407e-06,\n",
      "         7.56655382e-06,   5.00383224e-06,   2.83633215e-06,\n",
      "         1.67170878e-06,   1.19350982e-06,   9.87252978e-07,\n",
      "         9.54096555e-07,   1.05979705e-06,   1.18957478e-06,\n",
      "         1.23432375e-06,   1.22190029e-06,   1.20084648e-06,\n",
      "         1.18287498e-06,   1.14801849e-06,   1.08643614e-06,\n",
      "         9.36511867e-07,   6.17342678e-07,   3.35507108e-07,\n",
      "         3.19646379e-07,   2.60733349e-07,              nan,\n",
      "                    nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02\n"
     ]
    }
   ],
   "source": [
    "print N2_GS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dx_GS = gsw.earth.distance([GS[0]-.5,GS[0]+.5], [GS[1],GS[1]])[0][0]\n",
    "dy_GS = gsw.earth.distance([GS[0],GS[0]], [GS[1]-.5,GS[1]+.5])[0][0]\n",
    "dx = 4e3; dy = 4e3\n",
    "Nx_GS = 100\n",
    "Ny_GS = 100\n",
    "k = 2*np.pi*fft.fftshift( fft.fftfreq(Nx_GS, dx_GS/20) )\n",
    "l = 2*np.pi*fft.fftshift( fft.fftfreq(Ny_GS, dx_GS/20) )\n",
    "\n",
    "k_GS = k[np.absolute(k) < 5.*Rd_GS[1]**-1]\n",
    "l_GS = l[np.absolute(l) < 5.*Rd_GS[1]**-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85800.2983284 111194.926645\n",
      "5.82748556522e-06 0.000193893247747 1.16549711304e-07\n",
      "(100,) (100,) (100,)\n"
     ]
    }
   ],
   "source": [
    "print dx_GS, dy_GS\n",
    "print (2*dx_GS)**-1, 5.*Rd_GS[1]**-1, (Nx_GS*dx_GS)**-1\n",
    "print k_GS.shape, l_GS.shape, k.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50,) (50,) 85800.2983284 111194.926645 (49,)\n",
      "<xarray.DataArray (Depth_c: 49)>\n",
      "array([  -10.00006115,   -20.00006114,   -30.00006113,   -40.00006112,\n",
      "         -50.0000611 ,   -60.00006109,   -70.00256358,   -80.01506451,\n",
      "         -90.06006328,  -100.20256308,  -110.5900682 ,  -121.51007976,\n",
      "        -133.44509256,  -147.10512803,  -163.43519352,  -183.5678012 ,\n",
      "        -208.72298023,  -240.09073926,  -278.70109317,  -325.30655719,\n",
      "        -380.30712264,  -443.70276088,  -515.09343642,  -593.72659767,\n",
      "        -678.57216844,  -768.44015906,  -862.11055267,  -958.45325725,\n",
      "       -1056.53586028, -1155.72595435, -1255.87608991, -1357.68378579,\n",
      "       -1463.12934261, -1575.64299213, -1699.66489761, -1839.65538686,\n",
      "       -1999.10931291, -2180.15155508, -2383.76440293, -2610.28273438,\n",
      "       -2859.78914881, -3132.29607118, -3427.80347989, -3746.3113517 ,\n",
      "       -4087.81966191, -4452.32838443, -4839.83749199, -5250.34695635,\n",
      "       -5683.85674858])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "    Latitude_t   float32 39.5\n",
      "  * Depth_c      (Depth_c) float32 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray 'Depth_c' (Depth_c: 50)>\n",
      "array([  5.00000000e+00,   1.50000000e+01,   2.50000000e+01,\n",
      "         3.50000000e+01,   4.50000000e+01,   5.50000000e+01,\n",
      "         6.50000000e+01,   7.50050049e+01,   8.50250015e+01,\n",
      "         9.50950012e+01,   1.05309998e+02,   1.15870003e+02,\n",
      "         1.27150002e+02,   1.39739990e+02,   1.54470001e+02,\n",
      "         1.72399994e+02,   1.94735001e+02,   2.22710007e+02,\n",
      "         2.57470001e+02,   2.99929993e+02,   3.50679993e+02,\n",
      "         4.09929993e+02,   4.77470001e+02,   5.52710022e+02,\n",
      "         6.34735046e+02,   7.22400024e+02,   8.14470093e+02,\n",
      "         9.09740112e+02,   1.00715503e+03,   1.10590503e+03,\n",
      "         1.20553503e+03,   1.30620508e+03,   1.40914990e+03,\n",
      "         1.51709497e+03,   1.63417480e+03,   1.76513477e+03,\n",
      "         1.91414990e+03,   2.08403491e+03,   2.27622510e+03,\n",
      "         2.49125000e+03,   2.72925000e+03,   2.99025000e+03,\n",
      "         3.27425000e+03,   3.58125000e+03,   3.91125000e+03,\n",
      "         4.26425000e+03,   4.64025000e+03,   5.03925000e+03,\n",
      "         5.46125000e+03,   5.90625000e+03], dtype=float32)\n",
      "Coordinates:\n",
      "  * Depth_c  (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "Attributes:\n",
      "    long_name: Depth of T grid points                                                  \n",
      "    standard_name: depth\n",
      "    units: m               \n",
      "    positive: down\n",
      "    point_spacing: uneven\n",
      "    edges: Depth_w\n"
     ]
    }
   ],
   "source": [
    "print u_GS.shape, v_GS.shape, dx_GS, dy_GS, zN2_GS.shape\n",
    "print zN2_GS, z_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.27671062527e-05\n",
      "1.76637107718e-11\n"
     ]
    }
   ],
   "source": [
    "etax = np.zeros(2)\n",
    "etay = np.zeros(2)\n",
    "\n",
    "print f0_meta.sel(Latitude_t=GS[1]).values\n",
    "print beta_meta.sel(Latitude_t=GS[1]).values\n",
    "# kwargs = {'v0':np.ones(len(zN2_GS)+1), 'num_Lanczos':int(1e10), 'iteration':int(1e7), 'tol':1e-5}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponential profiles\n",
    "\n",
    "#### w/out lateral viscosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi_GSx0, w_GSx0, psi_GSx0 = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_fit, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_fit, v_fit, etax, etay,\n",
    "                                                                   Ah=0., num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, 27) 392 (50, 27, 27)\n"
     ]
    }
   ],
   "source": [
    "print w_GSx0[0].shape, np.argmax(w_GSx0.imag[0]), psi_GSx0[:, 0].shape\n",
    "# print (np.absolute(psi_GSx0[:, 1])**2).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbe981e9050>"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hVHWtDQ+33w7/9V8wdy7I2zeaWZNzD4a1hUqTu0t7KaA7XGSf99ebAVTRi2Fm\nVnvPP5/sdTFjBrzlLXlXY2bWPwcMaxEDm0hdKVh0BYqVjzB55SNd7dnrel6ThIxeQ6XK8jApM6ut\nCPjc5+Af/gEOKNuvZWbWfDxEytpGMQyUhoTkWCZErHykx/PZI3fuNWQK6Bo2VTpkCui9bG0Xrypl\nrUvS/1V56msRsX9dizEALrsMHnkELrww70rMzKrngGGtraRHoa/hUNAdLpTuVxBTu0NG8fw5abDI\nzs2YxBwYmy5fW27p2h77YhR7Mhw0rOXsBnyun3ME/LgBtQx7Tz0FX/wi3Hqr97sws9bigGFtq1Kv\nhTKboemu7pABdPVmZEMGdPdm9JjwXaCk96KUezOs5dwdERf1d5KkoxtRzHDW2Qmf/CR89auw6655\nV2NmNjCeg2Etb9exs3tNyC4XLnRXJlzclT5K2rPzMkp7P3q8R7bnpJB+LTvZ2/MyrHVExL5Vnufh\nUXV21lmw3nrwpS/lXYmZ2cA5YFjrKZRvnpyGgkrhAugRLEpfl4YM6LnKVFHPTfhKinDIMLMheuQR\nOPtsuOgiWMef0mbWgvyjy9pW2XABcGfJI3Osr5CRPO+nFwMcMqwtSNpF0u2SVkpalT5WS1qVd23t\nLCLptfjGN2CrrfKuxsxscBwwrDUVKh/KLkFbNlwAK7K9GFWEDBhALwY4ZFg7+C+S/1fsBYxPH+9I\nv1qd3HQTLFqULE1rZtaqHDCsdRXWdM2/KLc0baVwUdQjZFS4ttxeGWbDxBbANyPikYh4IvvIu7B2\ntWYNfPnL8B//kcy/MDNrVQ4Y1jYmMbtyz0VGX8Gi3Pm932dO/yeZtb6LAK8W1UC//CW8/e1wyCF5\nV2JmNjReptbajsqFhDvLtJGEjVFT0+N71rEos9bzA+AeSWcAK7IHImKffEpqXy++CGeemQyRkvKu\nxsxsaBwwrH2VGRrVZ+9FBdmN+Hopt+meWXuYCSwEfgf8Peda2t73vgcf+pD3vDCz9uDfjKxtTF75\nSJ/zLqoOF3cBU7s34evxHpndvW34mDOo/80f6f+U5rYr8OaI8KpRdbZwIfzqV/Dww3lXYmZWG56D\nYS1vcummeFUEidvSB2SCR4VhVFUr9HeCd/W2lvIHYELeRQwHp50Gp54KW26ZdyVmZrXhHgxrWeV2\n8O6hQu/Fbb3PHPT7z13s3gxrWwuBWyT9jt5zML6ZT0nt5+67k8cFF+RdiZlZ7bgHw1pTdpO7lLK7\ndA9x3kXJa2YwAAAgAElEQVTpnhjgpWpt2HkDcD2wPrBV5jEmz6Laydq18MUvJvMv3vCGvKsxM6sd\n92BY6yn0buqxKV4fQ51Key9uAz6IV5MyKxURx+VdQ7u76iro7IRjjsm7EjOz2hpSD4akOZIulvRJ\nSW+RtLWkg2tVnFk1+upZqNXQqNJdvb0XhrUjSRvV8jzr2znnwBlnwDoeS2BmbWaoPRhHAM8B3wH2\nBkYCTwE3DPG+ZlXptYP3UCdqD1YB6Mjpvc1qZwWwaRXnLSP5eW+D9MADsGgRHHpo3pWYmdXekAJG\nRDwBIOn6iLgxfe4fl1ZfHQAj4Fi6loydXFwSNDu86c502BNJT8YH0+ZsT0axrXhe1/Xp6+IytaX7\nYMxhUs8J3h39Fe0VpKwlbCjp4irOW6/ulbS5n/0MPv95GOGBymbWhmr1o22MpNNJei680J7VyY2w\n8CCKfzidyx5wbHpoJBw/9UJ6bIA7la7J2qOKweFO6DXcuUKoKCqGizlMZjaTe4aLjhHd4aIjc9HC\nlT3rNmsN363yvB/UtYo299xzcOWV8PjjeVdiZlYfVQcMSRMj4v5yxyLil+nci38m2fXVrE5KQkZh\nMoxND6Uho4epPVeCyoaOrtf0DBXldu3OhosuVYULs9YREWfmXcNw8OtfJ0Oj3vrWvCsxM6uPgfRg\nfAn4RKWDEXEDnnthA3JQ5vlA/sqfCRnTRzB3es+Q0WvS94eTL10TtSv0UBSV27U5Gy7mLp5cOVyU\nq9XMLNXZCeedB1dckXclZmb1M5CA8RFJZ0TE4nIHJe0YEe7wtSodVOZ1bUJG97yMnkFjzsikvTSA\nVAoUPc+pMlx4aJSZ9eGGG+Btb4Pddsu7EjOz+hlIwPgkyd+Cf1Z6QNI6wPeAj9aoLmtrpeFisPro\nyaA7FJQuKVsaHsrpMRQqNbBwYWbW27nnwokn5l2FmVl9VR0wImKmpJGSjoqIy6FrLfTPkAyfGtvn\nDQZJ0oHAOSR7dsyIiH+rx/tYo/QVLgbaiwE9QkbHiGRORom5mUCx69jB7cY98HDh3gsrr5qfaZIK\nwNkkqzX9NSL2zhxbB5gNLI2Ill21T9K3gcOAtSTL434qIp5Jj50OfBpYA5wSEbek7ROBC4ENgRsi\n4tS0fX3gYmAS8CxwZKXe9jz96U8wdy5cfXXelZiZ1deAVpGKiJWSlkjan2TtnROAl0k+LHv/yXeI\n0g/Sc4F9geXALElXR8RjtX4vq7cywWJcZhn9rl/QhxAypo+EwoiyO30XzWWPng2FNdW9hcOF1UA1\nP9MkbUbSU7x/RCyT9JaS25wCzKO6/SqGUuv6wKeAXYGNs8ci4thy1wzQWRHxzfS9TgK+BXxe0gSS\nPZbGA2OA2yTtEBEB/ByYFhGzJN0g6YCIuBmYBqyMiB0kHQmcBRxVgxpr6mc/g898BjbcMO9KzMzq\nayCrSJ0YEedGxF2SvgscDJwEXBERnWU+BGthCrAgIhalNVxO8hcvB4yW0k+4KL6uRchgZM8AUOjn\nso4RVZ5X8rXI4cIGppqfaUcDV0bEMoCIeLZ4QNIYkp+93yXpOa6ni4BdgGtJehhqKiJeybx8I0lP\nBsChwOURsQZ4StICYIqkRcAmETErPe9i4HDgZpJ/w2+l7TNJQlxTeflluPRSePDBvCsxM6u/gfRg\nHJpuqLcQ+CawJCL+q3gw+yFYQ6OBJZnXS0k+oK1lVBEusu01CRkZC/s4PVtHB9WHjK57O1zYgFXz\nM21HYD1JvyfpOfhJRFySHjsb+AqwWb0LBQ4ExkXEC/V6A0nfIdnN5gWgOAxsNHBP5rRladsakn+v\noqVpe/GaJQDpH7xekDQyIppmYtSll8I++8BWW+VdiZlZ/Q0kYLwf+LOkp4BbgSck/WNEXAXJuOKI\nuKkONVbpyszz8cCEvAqxLiW/7JcLFoXShuw5x1SYON3XL/ID+CW/K3wc1P26tMZC+rWj9FqHi+Y1\nD5jf8Hd9vONpHu94pha3GgFMBPYh+cv+PZLuAXYCVkTE3HSOhirfoiYWAxsM5QaSbgVGZZuAAL4W\nEddGxNeBr0v6KkmP+PShvF/J+1Q0fXr32xQKBQqFQo3etrKZM+Gkk+r+NmZmA9LR0UFHR0fN76tk\nWGsVJ0rTgfOA/YAPkowhHg08ANwGTKj1hENJewDTI+LA9PVpQJROipQUcGkt39qGrMpwUUifd/Rx\nq76OlbNwJYP7hT9Tc6Velq77ZzlcNLdPEBGD/mVcUkyLgY+4maETe71vNT/T0l+2NyxueifpVyT/\nkU0i2YtoDbARsAlwVY3mQxTfe5/My/cAHwN+TMkQqYi4vVbvmb7vVsD1EfHu0n8TSTeRDH9aBPw+\nIsan7UcBH4iIzxfPiYj7JK0LPB0Rb6vwXlHt516tvPgijBkDTz8NG2/c//lmZnmRNKTPzKKB9GCc\nk3aVX5Y+kLQT3YHjg0MtpoxZwPaStgaeJpm09/E6vI/V1ADDBfQ9PKl4rKPa9x+ZDpUaxBCrrt6M\nlb3rHnBvilkv1fxMuxr4afqL8gbA7sCPIuJK4AwASR8AvlzLcJGaUabteyWvA9h2qG8kafuI+HP6\n8nC656FcA1wm6WySP2JtD/wxIkLSi5KmkPw7Hgv8JHPNPwH3kYSimgagobrlFthzT4cLMxs+BrJM\nba9xuBHxJ+BPwLmSSj+EhiwdS3sicAvdSzo2fuyDDcAAwkW1KzhlryvVUeY/4QLJilK1DBkOF1YD\nlX6mSTo+ORznR8Rjkm4GHgI6gfMjYl6D6hvXiPdJ/UDSjiSTuxcBn0trmCfpCpKxbquBEzJdDl+g\n5zK1xWG5M4BL0gnhz9FkK0hdey18+MN5V2Fm1jhVD5Hq90bSpIiY0/+ZtechUs2kn2FGBXqEi8Hu\nS1Fq7uKS/S86RiSjuWsxXKosh4vW0TxDpFpJunzuYWXar4qIf8yjplpo9BCpzk7YYguYPRu23rph\nb2tmNigNGyIl6W3AyxHx977OyytcWDMZeLgo3WW71GTKB5DS3bgnje2+zxwmJRvuTR9Rm56MXu1m\nw8LeFdoLjSyi1d17L2y5pcOFmQ0v1QyR2hT4l3Q88NUR8X91rsla0uDDRaUQMalCe/bYHHrv3A3A\nWJKQURhBTeZkdL02a2/pDtsA62eeF21LMpzJqnTddXDIIXlXYWbWWP0GjHQS3v+TtAFwuKTzSHag\nvTQinqpzfdYS+hlOVKBsuCgNFqWBYvLKRwDQXT1vF1Mz55CcM3vkzj3u8ws+l4SM6WlPxqBDRpHD\nhQ0bxZ0a1sk8h2Ry9xJqt5TssHDddfDLX+ZdhZlZYw1kkvfrwH8D/y1pS+AYSduSrNrx24j4W51q\ntFZSbh+JAhXDRTZUFAMFZEJFSbjocSwTNHbjkR7BY87I9L5DDhkOFja8RMRxAJLujgj/ajwETz0F\nK1bAbrvlXYmZWWMNZJnaLhGxHPh3AEm7A2dKGkEyhOr3NazPml4fQ6MK9BitXS5c9Bkq7ky+rCgJ\nGaOmZo7v2d2uu+gKHcdPvbB7z76a9mSYDQ8R8UtJOwBHAFuS9FxfEREL8q2sdVx3HRx8MKy7bt6V\nmJk11qACRlZE3AfclxlC9QtgUUTUfNlaazZVhovCmj7DRY9gURIqbiv3tndlNl1JzysNHaJMyCjs\nkb5wyDDrj6SjgfOB60nmXbwLOE3S8RHxm1yLaxHXXgv//M95V2Fm1nhDDhhFJUOoNqvVfa3F9bPX\nRV9DofpyG4PY2bGwpvy+GWZWzneAg7MLe0h6P3AJ4IDRj5dfhrvvht/+Nu9KzMwab51qT5T0LUmF\ndChUtn0DST2W8omIF2tVoLW4fn6h75o3UfyaDnkq9khUChHZ9lFTy5+TnfhdTS1m1sMmwD0lbfcC\nb8yhlpZz223w3vfCppvmXYmZWeNVHTCAw0hWD1kh6RpJJ0raIe25GCHphLpUaG2ldP+Kweiz5yIN\nKFESOnptxGdm/fkR8D1JGwJI2gj4btpu/bjjDth337yrMDPLx0ACxhkRUQC2Bn4FvAO4QdKTwGeA\n99a+PGteJUvTLlyZSxU9ei+KE75LwsUcJjWqHLN2cgJwKvCSpBXAi8AXgc9LWlx85FphE5szByb7\n7xpmNkwNZJnam9KvrwDXpA8kjQM+ANxfjwKtfcxhUo+du2eP3LnHKlJMJZmLsSdwZxIeVqQTuouT\nvSsOjcqsJtX9fpNr0mNiNkx9Iu8CWlVnJ8ydCxMn5l2JmVk+arGK1EJgYQ1qsXbTQY9laiuJqb03\n0yunqkndaejoNf/CzAYkIu7Iu4ZW9fjj8La3wZvelHclZmb5GMgQKbOamTPAnoXScNHX0KjS+Rdm\nNnDpAh7flfSkpBfTtv0lnZh3bc1uzhyY5JGZZjaMeVkda4i5iyez69jZ/Z9YYZhURRXmXZjV0jCd\nx3M2MBo4hu5NYx5N28/Nq6hWMHu2A4aZDW/uwbCmMJBeh1ElS9pWfZ+O9LFwJd5kz6xf/wAcHRH3\nAGsBImIZSeiwPniCt5kNdw4Y1nDZidfVzJXIDocqu+dFydCo2SN37prgPYdJyRK13gPDbKBWUdLL\nLemtwHP5lNMaPMHbzMwBwwbloP5P6ajB25Tpoeh1zEOjzOrlt8BF6UqBSHo7ydCoy3Otqsl5greZ\nmQOGDcoAhxdleg+yY9mLE72LvRi9dvXOGDW1zNCozHkx4NWjqghJZsPbGSQrBD4MbA4sAJYDZ+ZZ\nVLPzBG8zMwcMG7QyIaPcZnsd3U+zu2mX7k9RMWTsSc+ejDIrRg08XJhZfyJiVUR8MSI2BkYBm6Sv\nV+VdWzNzwDAzc8CwIakQMopBoyPT3k8vBlQIGeWCRpk5F9lwkZ1/YWaDI2mCpOMlnQ78IzA+75pa\ngQOGmZkDhg1ZFcOlOrqfluvFmMPkssOlyg6ZKhMuskr318i+n5n1T4lfkwyNOgM4FPga8JCkCyQp\n1wKbWGcnPPCAJ3ibmTlgWA1UO1yqdy9GdqhUNmT01ZtRLlxkQ0pf72tm/fpnoADsERFbR8R7I2Is\n8F7g/cDxeRbXzIoTvEeOzLsSM7N8OWBYjdxI2aDRUfKV3r0K5UIG9B4yVWm+RWmwKJ3f0cs4f/qb\n9eGTwMkRMSvbmL4+NT1uZTz8MOyyS95VmJnlzwHDaqyfIVMV5mKUhoxKK0xlezfK9Vp0D7vyIGiz\nQZoA3FHh2B3pcSvjySdhu+3yrsLMLH8OGFYHacgonezd0X1GsRejNGT0NWSqv16L0p6LHj0lHZhZ\nddaNiJfLHUjb/blRwcKFMG5c3lWYmeXPg9Ot8TpGQGENcxdPZtexs7tCxiTmAElYmMxsIAkSk9Ln\nxddZ/Q6HMrOBWk/S3kClydz+3Khg4UI4/PC8qzAzy58/KKxObgQOSnoxxo1MehAKdH8tYw6TeoQM\ngMnMLjt5u69g4eFR1swkHQicQ9ITMCMi/q3CebsBdwNHRsRVadsXgWnAWpJVno6rw74UfwF+3c9x\nK+PJJ92DYWYG7uq2vKRzMeYuntxjKFNpOOi1IV+ZoVBZvcJFnytIeTdvayxJ6wDnAgcA7wQ+Lukd\nFc77AXBzpm1L4CRgYkS8m+QPREfVusaI2CYixvX1qPV7toPOTliyBLbZJu9KzMzy54BhddTPXIzM\nL/+lIaPc3IxKwaJ4fvYa739hTWoKsCAiFkXEauBy4LAy550EzKR3b8G6wBsljQDeACyvZ7FWvWXL\n4M1vhg03zLsSM7P8OWBYnQ0sZPTVm1GqNFRU1NHXQfdiWEONBpZkXi9N27qkPRWHR8TPycyDiIjl\nwA+BxcAy4IWIuK3uFVtVPMHbzKyb52BYA6TzMSpJJ30XFSd/A70mgFcTKLpCSl/Do8aNLL8ZoNkg\nvdxxP6903F+LW50DfDXzWgCSNifp7dgaeBGYKenoiPhNLd7UhsYBw8ysmwOGNU5fE76LYSANGtmQ\nAf0Hi6ENiTqIfvfvsGGtqv++tp0M22Zenzmj3FnLgLGZ12PStqzJwOWSBLwFOEjSamB94MmIWAkg\n6SrgfYADRhNwwDAz6+YhUtYgfQyV6sic1seQqVLF42XPKd6no/eh8jxUyhpiFrC9pK0lrU8ySfua\n7AkRsW36GEcyD+OEiLiGZGjUHpI2TMPHvsD8BtdvFThgmJl1cw+GNVCFpWuh394Ms3YQEZ2STgRu\noXuZ2vmSjk8Ox/mll2Su/aOkmcADwOr0a+n5lpOFC+G44/KuwsysOSgi+j+ryUkKuDTvMqxqaW/B\nuJHJ10LJ4ezrzNyMqvXVe1Ha1msehodKtZ9PEBGVNo3rl6Rg0eqBX7j1ekN6X6sPSVGPz70xY+DO\nO71MrZm1Nkk1+exyD4bloExPBlTVm9FDn3tcmJk1xuuvw1//moQMMzPzHAzLTcmcDCiZi1FyeseI\n3o++lF5fSbEXpYvnYpjZwCxaBKNHwwj/zcPMDHDAsGbQV8jowMzamKQvS1oraWSm7XRJCyTNl7R/\npn2ipIckPS7pnEz7+pIuT6+5R9LY0vepp6ee8gRvM7MsBwzLUWa+w8KVPVeY6sicln3eEO7FMGsE\nSWOA/YBFmbbxwBHAeJL/M56XrpoF8HNgWkTsCOwo6YC0fRqwMiJ2INlH5KwGfQsArFgBW2zRyHc0\nM2tu7tC1nBVDRvpLfXFeBvReZWqoanEPM6uls4Gv0HOp3sOAyyNiDfCUpAXAFEmLgE0iYlZ63sXA\n4cDN6TXfSttnAuc2oviiv/4V3vrWRr6jmVlzcw+GNYmS3oyijhrcumMw93Evhlk9SToUWBIRD5cc\nGg0sybxelraNBpZm2pembT2uiYhO4IXskKt6c8AwM+vJPRjWRNLVpaB3Twb0Xs62Px39nmFmdSTp\nVmBUtolkb4+vA2eQDI+qy1vX6b5l/fWvnoNhZpblgGFNpkLIgL4DQ6HkdV/nmllDRETZACFpZ2Ab\n4MF0fsUY4H5JU0h6LLKTtMekbcuArcq0kzm2XNK6wKYRUbrJTZfp06d3PS8UChQKhYF8W724B8PM\nWlVHRwcdHR01v6832rMmVTJEqddysjXSa6O9Ut54r/V5o71mJ2khMDEinpc0AbgM2J1k6NOtwA4R\nEZLuBU4GZgHXAz+JiJsknQDsHBEnSDoKODwijqrwXjXfaO9974OzzoI996zpbc3MGs4b7VmbKzP5\nG+oXNMwsT0E6rCki5km6ApgHrAZOyCSCLwAXAhsCN0TETWn7DOCSdEL4c0DZcFEv7sEwM+vJPRjW\nAvqYcD3UwNFvDwa4F6PVuQfDutWjB2PzzeHJJ2Gk//5hZi2uVj0YXkXKWkAfv+AX98+oKiiYmdXW\nqlXwt78lIcPMzBIeImUtomTIVDl1CxkH4V4MMyvn2WfhzW+GdfznOjOzLk0bMCSdBXwYeB14Ajgu\nIl7KtyrLX/YXfe9VYQ3S0bQ/Ki1nnn9hZtZbM//N5RbgnRGxK7AAOD3neqzp3Jh5mJk1ngOGmVlv\nTRswIuK2iFibvryXZM1zswocMsys8RwwzMx6a5V+/08Dl+ddhDW7zCZ9NbufmVllzz7rgGFmVirX\ngCHpVmBUtolkPfSvRcS16TlfA1ZHxG/6vtuVmefjgQk1rdVaRRWTwau+h7WmecD8vIuwYcI9GGZm\nveUaMCJiv76OS/oUcDCwT/93+0hNarJ2URoS+gscDhXtYwI9/8Dwu7wKsWHgr3+Fd70r7yrMzJpL\n0w6RknQg8BVgr4h4Pe96rNU5QJhZ7a1c6Q32zMxKNe0kb+CnwMbArZLul3Re3gWZmZllvfwybLpp\n3lWYmTWXpu3BiIgd8q7BzMysLy+/DJtskncVZmbNpZl7MMzMzJraSy85YJiZlXLAMDMzGyQPkTIz\n680Bw8zMbJA8RMrMrDcHDDOzBpJ0oKTHJD0u6atljh8q6UFJD0j6o6SpafsYSbdLelTSw5JObnz1\nVspDpMzMemvaSd5mZu1G0jrAucC+wHJglqSrI+KxzGm3RcQ16fnvAq4g2T10DfCliJgraWNgjqRb\nSq61Blq1CiJggw3yrsTMrLm4B8PMrHGmAAsiYlFErAYuBw7LnhARr2ZebgysTdufiYi56fNXSLYr\nH92Qqq2s4vAoKe9KzMyaiwOGmVnjjAaWZF4vpUxIkHS4pPnAtcCnyxzfBtgVuK8uVVpVPDzKzKw8\nBwwzsyYTEf8TEeOBw4HvZI+lw6NmAqekPRmWE68gZWZWnudgmJnVwvwOeKyjv7OWAWMzr8ekbWVF\nxJ2StpU0MiJWShpBEi4uiYirh1ixDZFXkDIzK88Bw8ysPx3VnFSANxUyr88sd9IsYHtJWwNPA0cB\nH8+eIGm7iHgifT4RWD8iVqaHfw3Mi4gfD6B6qxMPkTIzK88Bw8ysQSKiU9KJwC0kQ1RnRMR8Sccn\nh+N84COSjgVWAX8HjgBIl6s9BnhY0gNAAGdExE15fC/mIVJmZpU4YJiZNVAaCHYqaftF5vlZwFll\nrrsLWLfuBVrVPETKzKw8T/I2MzMbBA+RMjMrzwHDzMxsEDxEysysPAcMMzOzQXjlFdh447yrMDNr\nPg4YZmZmg/Dqq/CGN+RdhZlZ83HAMDMzG4TXXoMNN8y7CjOz5uOAYWZmNgh//ztstFHeVZiZNR8H\nDDMzs0FwD4aZWXkOGGZmZoPgHgwzs/IcMMzMzAbBPRhmZuU5YJiZmQ2CezDMzMpzwDAzMxsE92CY\nmZXngGFmZjYI7sEwMyvPAcPMzGwQ3INhZlaeA4aZmdkguAfDzKy8EXkXYGbW9DryLsCakXswzMzK\ncw+GmZk1nKRvSVoq6f70cWDm2OmSFkiaL2n/TPtESQ9JelzSOZn29SVdnl5zj6Sxjfge3INhZlae\nA4aZmeXlRxExMX3cBCBpPHAEMB44CDhPktLzfw5Mi4gdgR0lHZC2TwNWRsQOwDnAWfUuvLMT1qyB\n9dar9zuZmbUeBwwzM8uLyrQdBlweEWsi4ilgATBF0hbAJhExKz3vYuDwzDUXpc9nAvvWr+REcXiU\nyn0HZmbDnAOGmZnl5URJcyX9StJmadtoYEnmnGVp22hgaaZ9adrW45qI6ARekDSynoV7eJSZWWWe\n5G1mZnUh6VZgVLYJCOBrwHnAtyMiJH0H+CHwmVq9dV8Hp0+f3vW8UChQKBQG/Aae4G1m7aCjo4OO\njo6a39cBw8zM6iIi9qvy1F8C16bPlwFbZY6NSdsqtWevWS5pXWDTiFhZ6c2yAWOw3INhZu2g9I8s\nZ555Zk3u6yFSZmbWcOmciqJ/BB5Jn18DHJWuDDUO2B74Y0Q8A7woaUo66ftY4OrMNf+UPv8YcHu9\n63/9ddhgg3q/i5lZa3IPhpmZ5eEsSbsCa4GngOMBImKepCuAecBq4ISIiPSaLwAXAhsCNxRXngJm\nAJdIWgA8BxxV7+JXr/YKUmZmlThgmJlZw0XEsX0c+z7w/TLtc4B3lWl/nWRp24ZxwDAzq8xDpMzM\nzAZo1SpYf/28qzAza04OGGZmZgPkHgwzs8ocMMzMGkjSgZIek/S4pK+WOb6TpLslvSbpSyXHNpP0\nW0nzJT0qaffGVW5ZDhhmZpV5DoaZWYNIWgc4l2Sn6eXALElXR8RjmdOeA06ie5fqrB+TTG7+mKQR\nwBvqXbOV54BhZlaZezDMzBpnCrAgIhZFxGrgcuCw7AkR8Ww6mXlNtl3SpsD7I+KC9Lw1EfFSg+q2\nEg4YZmaVOWCYmTXOaGBJ5vXStK0a44BnJV0g6X5J50vyVm85Wb3ak7zNzCpxwDAzaw0jgInAzyJi\nIvAqcFq+JQ1fq1a5B8PMrBLPwTAz68/ClVWcdCdwV38nLQPGZl6PSduqsRRYEhGz09czgV6TxK0x\nPETKzKwyBwwzs5rYM30UnVXupFnA9pK2Bp4m2XH6433cVMUnEbFC0hJJO0bE4yQTxecNuWwbFAcM\nM7PKHDDMzBokIjolnQjcQjJEdUZEzJd0fHI4zpc0CpgNbAKslXQKMCEiXgFOBi6TtB7wJHBcPt+J\nOWCYmVXmgGFm1kARcROwU0nbLzLPVwBbVbj2QWC3uhZoVfEkbzOzypp+krekL0taK2lk3rWYmZmB\nJ3mbmfWlqQOGpDHAfsCivGsxMzMr8hApM7PKmjpgAGcDX8m7CDMzsywHDDOzypo2YEg6lGRJxofz\nrsXMzCzLAcPMrLJcJ3lLuhUYlW0CAvg6cAbJ8KjsMTMzs9ytXg2bbJJ3FWZmzSnXgBER+5Vrl7Qz\nsA3woCSRbEY1R9KUiPhL+btdmXk+HphQ01rNrFXMA+bnXYS1uVWrYITXYTQzK6spfzxGxCPAFsXX\nkhYCEyPi+cpXfaT+hZlZC5hAzz8w/C6vQqyNdXY6YJiZVdK0czBKBB4iZWZmTaKzE9ZdN+8qzMya\nU0v8/SUits27BjMzsyIHDDOzylqlB8PMzKxprF3rgGFmVokDhpmZ2QB1dsI6/gQ1MyvLPx7NzMwG\nyEOkzMwqc8AwMzMbIAcMM7PKHDDMzMwGyHMwzMwqa4lVpMzM8nVj3gVYk/EcDDOzyvzj0czMbIA8\nRMrMrDIHDDMzswFywDAzq8wBw8zMbIAcMMzMKnPAMDMzGyBP8jYzq8wBw8zMbIA8ydvMrDL/eDQz\nMxsgD5EyM6vMAcPMzGyAHDDMzCpzwDAzMxsgBwwzs8ocMMzMzAbIk7zNzCpzwDAzMxsgT/I2M6vM\nPx7NzMwGyEOkzMwqc8AwM2sgSQdKekzS49L/b+/eY+SqCjiOf39QURABCbFEa3mIxaooFoUqKtoC\nlhqLf/jAFz4RFZUAMeIrmviqRAWNIEERBTS8RG0UiCCgMVpQsIhQTQWLvIQA1gei4fHzj3vajnVn\nd3bvvXtntr9PMtmZM/fc+Z3ZM3fn7H0cfbDPMl+WtEbSKkl7T6buKJH0PkmrJV0vaXlP+YdK+1dL\nOrinfIGk35b2n9RTvpWkc0qdX0qa23b2bbaBrbZq+1UiIkZTBhgDubHrAC1Ju0bLTGzXTGxTf5K2\nAJkNfagAAApgSURBVL4CvAx4BvA6SU/bZJlDgKfYfipwJHDqoHVHiaSXAK8A9rK9F/D5Uj4feA0w\nHzgEOEWSSrWvAm+3PQ+YJ+llpfztwH3lPTsJOKHt/D/5CTz3ufXXc+WVV9ZfScOSaWLDlgeGL9Ow\n5YFkmk4ZYAxkddcBWpJ2jZaZ2K6Z2KZx7QussX2L7QeBc4BDN1nmUOBMANtXAdtLmj1g3VHybmC5\n7YcAbN9Tyg8FzrH9kO21wBpgX0k7A4+z/auy3JnAK3vqfKvcvwBYPA35GzGMXy6SaWLDlgeGL9Ow\n5YFkmk4ZYERETJ8nAbf2PL6tlA2yzCB1R8k84MWSVkq6QtI+pXzTdt7Oxvbf1lPe2/4NdWw/DKyT\ntGOb4SMior9ZXQeIiIhxaeJFhpOkS4HZvUWAgY9S/f15vO2Fkp4HnA/s3tRLN7SeiIiYAtnuOkNt\nkka/ERHRGttT/sIpaS2wyxSq3mV7503WtRD4hO0l5fHxVTx/rmeZU4ErbJ9bHv8eOADYbaK6o0TS\nRcDnbP+0PF4DLASOALC9vJRfAnwcuIXqfZlfyg8DDrD97vXL2L5K0pbAnbaf0Od18/ciImIcdf5m\nrjcj9mA08UZERIzF9q4Nru5XwB6SdgHuBA4DXrfJMiuAo4Bzy4Bkne27JN0zQN1R8n1gEfBTSfOA\nrWzfK2kF8G1JX6Q69GkP4GrblvQ3SftSvY+HA18u61oBvBm4Cng1cHm/F83fi4iI9s2IAUZExCiw\n/bCk9wI/pjoH7nTbqyUdWT3t02xfJGmppD8C9wNvHa9uR01pwhnANyRdD/yHasCA7RslnUd1ibEH\ngfd44672o4BvAo8BLrJ9SSk/HTir7AW5l2rwFRERHZkRh0hFRERERMRwyFWkJknScZIemSlXKJF0\nQpnMapWk70rarutMUzXTJiEDkDRH0uWSbiiTkb2/60xNkrSFpGvLYTERU1ZnAsPyXON9seakittL\nOr9sn2+QtF/HeY6R9Lsy0eG3JTUyzeBEmSTtKekXkv4t6djJtmc6M7W1va7zHpXnp71vT/B7a7xv\nN5Cp8f49QJ7XS7qu3H4u6VmD1p3GTHuV8sn3bdu5DXgD5gCXAH8Cduw6T0NtOhDYotxfDny260xT\nbMcWwB+pTsZ9FLAKeFrXuRpo187A3uX+tsAfZkK7etp3DHA2sKLrLLmN7m2Qzz/VpH0/Kvf3A1Zu\n8nyjfbFuJqpDwd5a7s8CtusqD/BE4Gaq82QAzgUOn6b3aCdgH+CTwLGTqdtBpsa313XydNy3+2Zq\num838HtrvH8PmGchsH25v6Tn89Zl3+6XadJ9O3swJudE4ANdh2iS7ctsP1IerqQaRI2imTYJGQC2\n/2J7Vbn/T6qZ6UZ57oMNJM0BlgJf7zpLjLw6Exi21RennEnVnuQX2T6jPPeQ7b93lac8tyXwWEmz\ngG2AO2rmGSiT7XtsXwM8NIX2TGumlrbXdd6jzvp2v0wt9e1amYqm+/cgeVba/lt5uJKNfaXLvj1m\npqn07QwwBiRpGXCr7eu7ztKitwEXdx1iimbaJGT/R9KuwN5UV8qZCdYP2HMiWNQ1lQkMb+9Zpo2+\nWCfTbsA9ks4oh7acJmnrrvLYvgP4AvDnUrbO9mU18wyaqY26ra+3we113Txd9e1+2ujbtTK11L8n\nm+cdbPz+NSx9uzfTBoP27Qwweki6tBx/t/52ffm5DPgw1bXYNyzeUcxJG6ddr+hZ5iPAg7a/02HU\n6EPStsAFwNHlvwcjTdLLqeaJWEX1WRqZz1PMLEPaF2cBC4CTbS8A/gUc31UYSTtQ/adzF6rDSbaV\n9Pqu8gy7Ydlep28Ppuv+LemlVFcLHJpzR/tlmkzfzmVqe9g+aKxySc8EdgWukySqw4iukbSv7bun\nMeKU9GvXepLeQrULddG0BGrH7cDcnsdzStnIK7tsLwDOsv2DrvM0ZH9gmaSlwNbA4ySdafvwjnPF\naBrk83878OQxlnkV7fTFOpmg2mP+63L/Aup/+aiT50DgZtv3AUi6EHgBUPcfUnW2221t82utt4Xt\ndZ08bW1n62S6jeb7dt1MbfTvgfKUE7tPA5bY/utk6k5zpkn37ezBGIDt39ne2fbutnej+oA8ZxQG\nFxORtIRq9+ky2//pOk8NGyYwK1d/OIxq8q2Z4BvAjba/1HWQptj+sO25tnen+l1dnsFF1DDI538F\nZa4N9Uxg2GJfrJPpLuBWVRMQAiymmhekkzxUh44slPSY8k+2xVTHYNc12e1273/g29rm18kEzW+v\np5yn477dL1MbfbtWJtrp3xPmkTQX+C7wJts31WjLdGSCyfbt8c4Az63vmfg3M3OuIrUGuAW4ttxO\n6TpTjbYsobqywRrg+K7zNNSm/YGHqa728JvyO1rSda6G23gAuYpUbjVvY33+gSOBd/Ys8xWqq6hc\nBywYYx2N9sU6mYBnly8Eq4ALKVd26TDPx6m+dP0W+BbwqOl4j4DZVMeNrwPuo/oyuG2/ul1mamt7\nXec96qpvT/B7a7xvN5Cp8f49QJ6vUU0Mem3pL1ePV3ea3qMxM02lb2eivYiIiIiIaEwOkYqIiIiI\niMZkgBEREREREY3JACMiIiIiIhqTAUZERERERDQmA4yIiIiIiGhMBhgREREREdGYDDAiIiIiIqIx\nGWBERERERERjMsCIkSdpD0lP6DpHRERERGSAEUNK0mxJn5a0fIDF3wn8o+1MERERETGxDDBiKNm+\nC7gamD/ecpIeDWxp+4Gesh0kfULSA5J+LOm9Pc+9qpSfLWlBaw2IiIiI2EzN6jpAxDj2Bi6bYJlX\nAj/oLbC9TtIpwMeAI23/CUDSjsBsYE/bf24hb0RERMRmL3swYpgtYuIBxgG2fzZG+UHA2p7Bxf7A\nwbZPzuAiIiIioj0ZYMRQkrQ18GTbqyW9XNKJku6XpJ5lngjc0WcVBwKXStpS0qeBx9o+ZxqiR0RE\nRGzWMsCIYfVCYI2kNwLXAscB8227Z5k3AGf3qb8YuAk4Alha1hcRERERLcsAI4bVIuABqkOdFth+\nZIxDm3a3vXbTipL2BJ4E3GT7VOAE4D1lr8iYJD1F0jWNpY+IiIjYTGWAEcNqEfAB4JPAWQCSnrn+\nSUn7AVf1qXsQ8BvbF5bH51FdxvYd47zevcANNTNHREREbPYywIihI2k7YI7tNcDf2XiexeKexV4N\nnN9nFQfSc3K47YeBk4BjJf1Pn5d0hKRDgE8BlzbTgoiIiIjNVwYYMYyeAVwMYPtu4OeS3gX8EDbM\nfTHL9v29lSTtI+kzwMHA0yUtKeU7AfsAc4HzJM0r5UuBnWxfDGyz/jUjIiIiYur0v+fMRgw/Sa8F\n7rZ9Rc31nAycZvs6Sd8HjrZ9SyMhIyIiIjZT2YMRo2hR3cFF8T3g+ZKWAWup9pxERERERA3ZgxEj\nRdL2wFG2P9N1loiIiIj4fxlgREREREREY3KIVERERERENCYDjIiIiIiIaEwGGBERERER0ZgMMCIi\nIiIiojEZYERERERERGMywIiIiIiIiMZkgBEREREREY3JACMiIiIiIhrzX9fXsiCk1qrPAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe93315290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w_GSx0.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(np.absolute(psi_GSx0[:, 0]), \n",
    "                    (len(zpsi_GSx0), psi_GSx0.shape[-1]**2))[:, np.argmax(w_GSx0.imag[0])], -zpsi_GSx0)\n",
    "# ax2.plot(psi[:, 1, 19], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\psi$', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### w/ lateral viscosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi_GSx10, w_GSx10, psi_GSx10 = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_fit, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_fit, v_fit, etax, etay,\n",
    "                                                                   Ah=1e1, num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbe93e3db90>"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IsIYMmeJdwEXh4+RMWyvCbdMJxqdE4jNwTRzj7s8Aj5jZpkUWeFjccUcQKk4/\nHQ5LTogsIlIQBYykQbovtWu56LZ/VRQuBjZIUKhLyJgSLiI1CRmp4SIRguL7SX2Y2aXhmInodlN4\n/6bYPp8Cnnb3/8zz0jmea2jdfTfsv38wDe1R6jAmIiXSGIy4vMNF2Goxc8aCoBLUWB1OzUruC9P1\nrOpwMwLyCAjRWIKqtFTKo3DRDJ83aJlKuIrxDm3DRcKwjskYde6+f6fXzewY4GCCKXkjK4BtYs+j\nmbbabY8fc5+ZrQ1s7O6r2l137ty5E48bjQaNRqPzGxlCy5fDvvvCxz8Oxx5bdWlEpK6azSbNZjP3\n81owbm64mZlz7IDvo8BwEZnyLXEzh2v3Y9Bw0cijEPHztV8Lo8rKdyeZw0W7BQVj77ndeyy6otxS\neU9+JiON6H71RDnLqsDHw0/q706sbJG0Ms6zk3D3vr/RNjNf88fej1vreQx03VEXLpr6f4HXuPsf\nY9t3Bb5PMHvWdOBSYEd3dzO7Gng/wWxcFwJfdfeLzewEYDd3P8HMjgIOd/fU7+zNzEfh714nK1fC\na18L73oXfOxjVZdGRIaJmeXyt0tdpAZdjTutsp4SLlqeVzkeo27hYsgsWjq7fbiIuu3Eb+3EXqui\nq1THcBH/nYi2NaeV2hWpa7hIlC25r7pLDYWvARsCl5rZdWZ2FkA4Y9YPgVsJxmWcEEsEJwLzgMXA\nEne/ONw+D9jMzJYAHwROLu9t1MuqVUG3qLe8ReFCRKoz3gEjz5aDRnQ/9dv42nTZGLJwUWTrxSwW\nVv/vUlHIaDdgGmj9nUj+fpQcMqBDuJgoE62vo5AxLMJZtLZ19z3C2wmx177g7ju4+y7ufkls+0J3\nf2l47Adi2//i7keG218Zzj41dp58Eg4+GA44AGK9wERESje+ASOvcJHyTW9SxwpdWeoaLjp0jypD\nr0GjY+gZ8L0kPx9FVJCnfBbjn9cGrZ+TNp+ZMirw0b9J21a/SPx5h989kVG3Zk0w1mKHHeDLXwZT\n5zwRqdB4Boy8xzy0CRnx7jSpg1Sj/eu48F5co6jzVhMu0gJFFDSyhI1cQ0aiK1WRIbRjuIjbj/Yz\nopU4c1PXkJEsW0r5RMbFZz8LS5fCd76jcCEi1Ru/Qd5FVubT1sBI6hQuloWTnmyT8/TtdZuONkMl\nvKjuUb12i+pUge5Yie00/qKdNgO/8+jKlTlcQOssUu0kxhkV2d2sp7JH4p+xbdfRIG+ZMIqDvM87\nD04+Ga7d/JsaAAAgAElEQVS5BrbYourSiMgwy2uQ93gFjDJaCjrMKJUpXMTlFTTqFDAyfsNfRMCI\nV4JnM3n+BXT/truvoFGTkNFXBT2LkkJGxwHfnUQ/SwUMiRm1gHHttfDGN8Jll8Huu1ddGhEZdppF\nqldldUNKdpeC1m4wzcR+kB4uou3RrV91CRfx1cwr0C5cpD3vdnxS2zDUz/tt012q365IhYWL2LmK\n7i4V/9lP6S7VSZ7vVaSGli+HI46AefMULkSkXsYjYJQ9xiEtZMSfR6/3Eh4GDRq9auR5rt4q2nm3\nXnQKF/Ht3YLGsIWMQsNFRCFDpBJ/+hMceih84APBvYhInYx+wKhqAHU8ZES3+PZ+w0Ivx/XbetHo\n87jUc1U7S1RcPEDMYgGzUgJFt6DRaSB4nULGQOGil9amJvUPGSIjZs0aeOc7g1aLj3606tKIiEw1\n+gGjSslwM2i4iBTZktHI6zzVdomKpM8YlU/QSFPU4PReQkbbcNFkaqtaUrhi98wZC7r/+8XPVUHI\nmFCDz5lImf7pn+APf4B//3fNGCUi9TTag7zrOP1rXuEgywDwXlswGv0UJO08/Vf48qygt+salRYo\nABa2GezdaRB4u4p06sDvHAd9Q/uQs5BZ3Reoa6RfK3mNlpW+46LzXUbwOYvOV/LA79Tpn+OawNmD\nDVgzM7/Wd+v5uD3tZg3yrqFhH+R9/vnBCt3XXAObb151aURk1GiQdzejHC7yPhfUIlzkqddwEb3W\na2tGTy0ZOXaVaqdjuLiM9uODYmWb0g0sWe74+aL7aFvJLRktP+d25RQZEcuXw4knwn/9l8KFiNTb\naAaMuoSL+CxQZQ7Qht5aLxo5XbPG4SIeHmavurnlNvX43lpRevqWvsDxGB0r8xl+J9JCUdsWpbTz\nNbtfowipZWyWXgyRQq1ZA8ccA+97H7ziFVWXRkSks9ELGFWHi7ICRd6L8Q0qh3CRR/eoqLIfb3WI\nB4a0QNEuZCSDRpbpbOOKGo/RTseVr6PA2WGF7nirQ0sXpGTXo/j5kuccsItUkSuDiwyzM86AP/85\nWFBPRKTuRi9gVKmsVopu4WKQtS/6UbOWi3ZdouJBwq4IbvHXsrRm9BoyypYpZDTaHx9vIWk7/iJ+\nvgLChUKGSKubboJ/+Rf43vdgmmZeFpEhMFoBo8rWi7qEi7LlFC4G/bY/GS7SukRF4sEi/jjad+q5\nB1uIr2wdQ0ajw4Gx8RNZxntMDuwmt3BR9NgNkWHz1FPw9rfDl78ML3xh1aUREclmdALGOISLuqlp\ny0XWVov49ri01oyWc9a8FQM6hIxu4i0WWWa9apB7uMgUbqQQZvbbjLdLqi7ruDjlFNhxRzj66KpL\nIiKSnRpbB1VmuKjz1LQVaRcukgFhSrCIns9pfd3nTO4ye9XNLNh0cnrSWSxoO5VtTxqrS1lhehYL\nWcgsZs5YEFTas163j7INOi1ty8xXYWBZyKxatQyNiVcA7+myjwFnlFCWsfe//wv/+Z9www1a70JE\nhosCxiDqFi7KlGPrRb/do7KEi7bBIv48FirsiqkhA2gJGtE1O62PURd9h4ysYmtn9BMGUqfVJQgc\nM2csUMgo35Xufk63nczsbWUUZpw9/DAceyz8x3/AZptVXRoRkd6MThepUZY1XJQ1uLsGXaNyCRdt\ntqd1o5q8bm9dpcqeSSpN392luskhXExI6Zql8Rjlc/d9M+53QNFlGWfu8J73wBFHwBveUHVpRER6\np4DRLw3qzkU/FfBu4WLKWIsrmBouLg9vbfbpNPi73aDvOn/T3lPIaNJ9HYmcwkXrStyUvmCfSB2d\nf34wc9QXv1h1SURE+qOA0Q+Fi1wUFS5adAsWlydeT4SM5FS2SX0P+K6gFShTyGgyueJ3s82J8g4X\nyS5b0XUVMiplZrub2a/MbJWZ/TW8PW1mf626bKPsqafgYx+Ds86C9devujQiIv1RwOhVXcNFP92j\nGn0ck5PCw0W7Vos0aa0ZMWldpnpd7bsupoSMuGbKAcltRYWL6DrRbHDR80FDRtULb6YwswPN7HYz\nW2xmH095/VAzu8HMrjeza81sTtp5SvCfBL8NrwF2CW8vDu+lIF/9Kuy+OzQaVZdERKR/Chi9KGN1\n7ugm/UsbQ5EIFys7jLNoe44M0irdbcNUY3XrrZ2cWztaQkYv5y665WIMmNlawJnAG4CXAG81sxcn\ndrvM3Xd395cDxwHfKbmYkS2BT7v7ze5+Z/xWUXlG3oMPwpe+FNxERIaZAkZd1D1U1GDWqEwyhItI\n15CRIks3qXYho+v7TgsbKT/3+LiFfrsNpYaMRvhicoXuRDkKCRfxa6dcd9BpcGtkT2CJu9/r7k8D\n5wGHxXdw9ydjTzcE1pRYvrhzAM0WVaK5c4NF9XbaqeqSiIgMRgEjqyJbL6JwEa/YFa1R0nVK0nbm\np5Rw0TZYtOtC1YdZLOytNSOpS6tG4SEj/jhWyS+05aKRvB+5cAEwHVgWe7483NbCzA43s9uAnwHv\nKqlsSV8EPmdmt4RjMSZuFZVnpN12G/zwh/DpT1ddEhGRwSlgVC0eLiJlhYwK5N16kdaiMCFDuOjY\nitFhVqms+m7NyGDR0tkDD4BuKV88ZDRatxUeLibKMPW6U8o5Btz9f9x9F+Bw4J8rKsb5wN3AN4Dv\nJ26Ss499DE4+GZ73vKpLIiIyuPHrBF0nyXDRCO+b4bYaDlCti9RB1vEQ0EvLRdzlwN7Zy5BlZe9o\nsbuktJDRMm1rRoMuSjeLhTCDqQvxlR0uIjUMF1n+nRc372dx84Fuu60AZsSebx1uS+Xul5vZC81s\nU3cvcWVPAGYCz3N3zRpVsP/9X7j11mB6WhGRUaAWjCyK6B7VLlzEH9elJSOn8RelLTrXY7jI2oox\niKyV5Kh1o9dWjjxaMlqmr1W46NlOja04ZO7LJ25tzAd2MLNtzWxd4Cjgp/EdzOxFscd7AOtWEC4A\nfgfsWsF1x8ozz8BHPgKnnQbrrVd1aURE8qGAUYV24SLe7z7aVkTIaHTdI3d5hYu0dScsj5aLuC5j\nMTp2y+qgn8pyZSEDhYsiuPszwEnAJcAtwHnufpuZHW9m/xDu9rdmdrOZXQd8DTiyouLeDVxiZt80\ns8/GbxWVZySdey5suCH87d9WXRIRkfyoi1Q3RQ3u7jBLz0Q3lQbqLtVBS0W/TYjIGi5WXgFbdFlt\nwK4AH3BFgqjS3EsImDljQeauU3l1l+pXarhohi82Ohw4BuEi4u4XAzsntn0z9vhLQB0mKt0AuBBY\nF9gmtt2rKc7oeeIJOOUU+PGPwazq0oiI5EcBo2zbbNp1Ks6WvvANJkMGlB80cugeVVrXqIRO4eIX\n4f1BWU50BZDzUmdZK9BRhb1jyEiMmYgfO0hFvddjO4YLCB432h8/DuFimLj7sVWXYdSdcQa85jWw\n115Vl0REJF8DdZEys4Vmdq6ZvcPMNgv7FR+cV+FGToZw0fI4berQQbtMNbruMVTadY/KEi6SVnbp\natVJWtetPHStbMe7HyVWvYb+uku1m2K3k47doi5j6grdcQOusSH5MbP189xP2nv6afj61+FTn6q6\nJCIi+Rt0DMaRwPuBvQia9M8ADhi0ULWS1+raacendAtpWZsgtk/L1KHJKW2zho5G1z1yldd0rEmp\nM0jF9BIu2oWNOprys4wq8c2p2/JYJyOrtuGiyWSwWLYqPWSkBGyp1MqM+7Wd+Uqy+clPggX1dtut\n6pKIiORvoC5S7n4ngJld6O6/CB8fmkfBaiktJGQdo7Fs1dTjm9Ogsbql33yk44rHTbKFika2ohWh\nyArjQmZPhAyfE2vF2Bu4vHUsRTJsdOoS1TIGI+NUtZEFGaYxHVRLF6nk56JJbJrj1s8VDN5dKrO0\n0BNZtgouC1vxmoSf5WktLRj9hKGeB5NLJ88ys3Mz7LdO4SUZcWeeCe97X9WlEBEpRl5/kbc2s08A\nFwEvyOmcw2GbTXsbCH4ZHStY0GeFqZF916IVFS6iyufsaP2JTYOB3j4HDIKxEmHIiKQN3I6Hjo6h\nInFsNMB7waa7heUJ/p2KDBepFe52n4smqSEDmBJguyk0jKT8DixqzO7rc5P6u9KMXUd69fmM+32x\n0FKMuBtvhDvvhMMPr7okIiLFMPdsE4KY2R7ufl2H1w8G3gj8xN1L/dNuZs42JU9skhx0nTVkpE1R\nG+smUqtvY/sY4F1GV5dZLJwY8zCLBROzSdkVdF63ot24iniwaDOYu4pwEVwn0f0I0ivTSY3ofvLf\nsNd/m14Hoi9aOntqC0ba70eH34F2Oi5K2C1cLDPcve85eszMv+HH9Hzce+27A11XimFmnvXvXlGO\nPx6mT4dPf7rSYoiITGE22N/MifP0EDD+n7v/n0EvWITSA0aye1KOIQMYPFwkK2v9nq/HgNFPuGi3\nynWW45Ihw9qFiyxT1aYEi+SUtFWFC+jQqtXscIJGdN9/UMwSMnoOGND6OzBAOTO1XChgSEzVAeOR\nR2D77eG222DLLSsrhohIqrwCRi81z781s0+6+9I2BdrJ3RcPWqDaS84C1WRynYqs3aWi8RhRVxGY\n7CrV46JkmfetQ4tIKFlp7SdkpHaXmtMmZGScYrbTGhe1DBfdNJnshgc9fWYGXU+jq/jvAPRXTnWL\nkiH03e/CgQcqXIjIaOulxvIO4E3A15MvmNlawL8Af5dTueopGS6ix00GCxkT5ykgXCSPKShodGu9\n6FZJ7WcBunjIgCAEzJ6TbZXtKIi0CxVRoGi9XoXhol9NWsdlZJWYfKDQkAH9lzM6FhQupPbWrIGz\nzoKzz666JCIixcr8l9zdzzezTc3sKHc/DybmQn838GEGWv+3PTM7EDidYErdee5+WhHX6arTyttM\nGyxkQNdFyCakhItulfuWSmoBrRlp1++3QtpPa0ZU4Z/FgtRgAIlVv2kNFu2Oias8XAzyb9ak90kA\nUmY4KyxkJGeW6kUzvFe4GDpm9lngMGANwfS4x7j7A+FrnwDeBawGPuDul4Tb9wC+CzwLuMjdPxhu\nXxc4F5gFPAS8pV1re5UuvRQ22ABe/eqqSyIiUqzMYzAmDjCbAzybYGjsCcDjBAFglru/M9fCBS0j\ni4F9gfuA+cBR7n57Yr9ix2B0DBdk73eeJm08RjsDrhnQc4U1Q0tJnuEiKWvQiK43mwVd18jIdt30\nEFHLcNEsqDCN6H7qWi1JfY3BiEv+DvSqW7jQGIy+hJX2Y4CZwIbx1/L4v97MNnT3J8LH7wN2dff3\nmtmuwPeBVwBbE/wL7+jubmbXACe5+3wzuwg4w91/aWbvBV7q7ieY2VuAI9z9qDbXrWwMxqGHBrd3\nv7uSy4uIdFX6GAwzO8ndz3T3K8zs88DBwPuAH7r7M2a22aCFSbEnsMTd7w3LcB7BN163dzwqTxlW\n3l7UCCtWDXpvyUiOx2iSHjJyWJCsZZaqbl2m+umGlbOsrRnRt+tFBoBadotqFlOWiXM3KKclI21M\nUlZquSjSOcDuwM/IvgBfZlG4CD2boCUD4FDgPHdfDdxjZkuAPc3sXmAjd58f7ncucDjwS4K/C58J\nt58PnJl3eQd1991w5ZVw3nlVl0REpHi99Ls4NFxQ727g08Ayd//P6EV3fyj30sF0YFns+XKC0FGO\nDOEiel5oyMjwLXI3UQW2JWRE5+6z+02RrRfJ83ULGmWEjKJ0fG9FDc7vVjFPWaslU8jo9/OUHJMk\ndXAgsL27P1LUBczsn4F3Ao8Arws3Tweuiu22Ity2muBvQGR5uD06ZhlA+IXXI2a2qbv3sEhRsb75\nTTj66KCLlIjIqOulJrAP8Hszuwe4FLjTzP7G3X8MwVgJd7+4gDJm8+jcycfrNeBZjf7Ok7bidhtT\nvmEfZHxDu+vmEC6SpoSMCs1O6dKUFhJKWYW6Aslw0VM3tka0X5eL9FNpb7MgZCktGYN6qgl/aQ5+\nHlkKrDfICczsUmCL+CbAgU+5+8/c/RTgFDP7OEGL+NxBrpe4Tltz505eptFo0Gg0crpsOnc4/3z4\n4Q8LvYyISM+azSbNZjP38/ZSGz4NOAvYn6Dq8T5gupldT1Ad2RXIO2CsoHXw+NbhtqmeM7fzmZIL\n49VNvGLViO7zDxd1khYuOm3PKgoovQ4WL/vn2zFc9KIR3jcHKU1Cl9a7wn5WeYSMZzVav2B47NTB\nzke/XeS+O/B1y2Zmr489PRe4wMzOINFFyt1/leV87r5/xkv/ALiQIGCsALaJvRb9v99uO7HX7jOz\ntYGNO7VexANGGRYvhj//GV7+8lIvKyLSVfJLllNPHfxvJvQWME4Pm8q/H94ws52ZDBz9DtHsZD6w\ng5ltC9wPHAW8teez7Jd43Clk5PENaq/SBrlWHS6idTkKkiVEJAdstxt8nXbuBczueUaqaN8yftZd\nw0WV65ZUFS6kDualbPuXxHMHXjjohcxsB3f/ffj0cCbH1v0U+L6ZfYWg69MOwLXhIO9HzWxPgr8N\n7wS+GjvmaOAa4M1ApgBUlp//HA45BGxoh/yLiPSml2lqp/TDdfc7gDuAM80s+UdoYGFf2pOAS5ic\npva2nk5SROzJU4dVvUe1QhdfgbsXve7fT8iA1sp/nj/7duXIrbtag8FbMaoOF1UEfJng7tuXeLkv\nmtlOBIO77wXeE5bhVjP7IXAr8DRwQmzapxNpnaY2ajWfB3wvHBD+R4Ivo2rjwgvhQx+quhQiIuXJ\n82vSH+V4rgnhH5Cd+zq4pUUgvG/SvRWjLB2mqB3XcJFcr6IfCzbdbeL8/YaMSL/HZZUaLnpcEK/l\nuAZTQ0bWz3vV4UJqxcwucPfDUrb/2N3/ZtDzu3vbhVnd/QvAF1K2LwRemrL9L8CRg5apCI88AgsW\nwL77Vl0SEZHydK3JmNnzgcfd/c+d9gv/46+PtHARPW5Sn5DRYY2NUavQpXWL6hYoohW320lbiXv2\nqpunhIyR1K4LW4PeWzIULmSq17XZ3iizEMPukktg7701e5SIjJcsX5VuDPxjOHDuAnf/bcFlGlxa\nuIiv+9Cg+pCxzaYdK3WjXKGLKv5p4aJtoIhvj4WKdvvPntMaMqD41oheDdR6kRYu+p3FTOFCYsIV\ntgHWjT2OvJCgO5NkFI2/EBEZJ11rI+EgvI+Z2XrA4WZ2FsGq2v/P3e8puHy96xQuosdVh4wMM0aN\nmmTXqChcdGyhyBI2ksLwYVdMhoz49esSMvqfMao1WLQs+Lh09tTPdzd1Chcaf1EX0UxNa9E6a5MT\nrDUxt+wCDatnnoFf/AI+97mqSyIiUq5eBnn/Bfgv4L/M7AXA283shQSzdvy3u/+poDJm1yFcRBWm\n1EpYmSEjPu6iTRlr841xTjNJ9RQuktsuz3CBvVOOD4PG7FU3w6bBDFR1CBkdg0XXdS/ah4voeduQ\nkfYZr1O4kNpw92MBzOxKd/921eUZZtdeC1ttBdtuW3VJRETK1dcgb3e/D/gygJntBZxqZtMIulD9\nOsfyZZchXESPKwsZdZyOtmCZwkWXULGyTYvFFlFXqTYhxAjGaFQZMvKbIapzuOhJht+VUfscSu/c\n/dtmtiPB4OkXELRc/9Ddl1RbsuGh7lEiMq4GnkXK3a8Brol1ofomcK+75z5tbSaN6D69wrSQWZ2/\n6S3aGM8YlTqgOx4eYkEhHip+kTjkoJR9IlvEB31fUU3I6CtUdGq9iIWLZLCIf2YyfbYVLiQjM3sb\n8C2CBfDuJZi96WQzO97df1Bp4YbEz38O3/hG1aUQESlfbtPUJrpQPSev89ZdNAXqRMUO0ltF2gzq\nrkWlLj44eIAuUVnfQ7eZoXJxOa1dpwqUWytFmRpVFyBB4y/q6J+Bg+MTe5jZPsD3CFbelg6WLoX7\n7oO99qq6JCIi5Vsr645m9hkza4RdoeLb1zOzlhqWuz+aVwF71ozug2JGlb+FzJr4xnqiQhhVqpsM\nJKpYz5yxYLKC3ghf3I9CwkX0fuLvq9O+kUVLZ6dXiBurewoXnb5JbycacJ02tWwnB7V5nNTSelFS\nuMhFt7EXsdeT/37Jz0Den+1uWq7bnBbewhejcL1sVbGFkKJsBFyV2HY18OwKyjJ0LrwQDjwQ1l67\n6pKIiJQvc8AADiOYPWSlmf3UzE4ysx3DlotpZnZCISXMKj5+ohndt4aMlsfJCtiA4y+6hoz48xzC\nRdq2drdIXt+09xouFvaxDsUWPYaQ1P1j26JwA/mui1Fa60UihKRdt+yWlCnhAhQuMjCzA83sdjNb\nbGYfb7PPV81siZktMrOZZZcx9G/Av5jZs8IyrQ98PtwuXfzud1pcT0TGVy8B45Pu3gC2Bb4DvBi4\nyMzuAt4NvCr/4vWoS8goKlxEOoaMifv8w0UWVYWLnnRodTiIzq0XYyFqIQi1/TdNa73IeQKDXMPF\nGHWPMrO1gDOBNwAvAd5qZi9O7HMQ8CJ33xE4Hvj30gsaOAH4IPCYma0EHgU+BLzXzJZGt4rKVnsL\nF8KsesyKLSJSul6mqb04vH8C+Gl4w8y2B14LXFdEAXt2GZMtBk3C8RCx6VYLCheRljEZjdmTA20h\nt3BR9DfVea3FsSA2oLqtObRd12KLOe1nkEru1yIWVHrtjtWLysZexD7PU8pQQtcotVwMZE9gibvf\nC2Bm5xG0Dt8e2+cw4FwIJtEws+eY2RbuvrLksv6fkq83Mh57DJYvh112qbokIiLVyGMWqbuBu3Mo\nS37ahQxi26L9kpatGvgb1ShkALHZfFa3vN6rIsNFv4FikNYLn9NmsPfeZFv7ItQ2XKQEi366arWT\n279DPytvx4+Lj5sZxnAxRq0XoekEi9VFlhOEjk77rAi3lRow3P03ZV5vlFx/PbzsZTAtt2lURESG\nSy9dpIZLWnep+OOC17yYxcLJintsGtDcwsXEgNppU7rOlCHXrlEdWhk6jcXo1HIRFx9/MXImKvkd\n/v1z+qyr5WK8hBN4fN7M7jKzR8NtB5jZSVWXre6uu07do0RkvI329yvJloz49hLMYiHMCCpkg4aL\nFu0qk2nbu8wM1U/rxSDhYsGmu6WvhxGXoRWj10HgeardtLRprXM5Sw+54X2/4WKIWi+yjH16vHkd\nTzS79hRdAcyIPd863JbcZ5su+5ThKwQtJ29ncjmaW8LtZ1ZQnqGxcCG8/vVVl0JEpDqjHTCgNWRU\nIAoZg4aLtgPUG11OkuieFVd2uMhLarhIab3oNP6irJW8O8q71amZsi2HMJ06BW50LbVcTNiosQcb\nNfaYeL7y1Hlpu80HdjCzbYH7gaOAtyb2+SlwIsGaQq8EHqlg/AXAEcAO7v4nM1sD4O4rzGx6BWUZ\nKgsXwkc/WnUpRESqM/oBA1pDRkmtF3GFhYvk47hGfJ/2IaPf8vVqIbMnVvSOtIzDyDjYO1O4GMWB\n3d00izmtwkW+3P2ZsIvRJQRdVOe5+21mdnzwsn/L3S8ys4PN7PfAn4BjKyruX0n8jTCzzYE/VlOc\n4fD443DvvbDrrlWXRESkOuMRMCB7sOjSbaOMlbczh4tOov0a0fPWkJHXTFGFSXSTatslqsdwkeca\nGJVqFn+JQsPFEHWPyls4I9/OiW3fTDyvwziH/wbOMbMPAZjZVsDpwHmVlqrmbrgBdtsN1lmn6pKI\niFRndAd5DyplcbxIWd2EOq7bkby104w/Lj9PplXo2w667rXloUO4iLpHRdfKYwapSlsvmolbFmmf\ni2hbh3O0XRk8OkbhYlx8kmCGwJuA5wJLgPuAU6ssVN1p/QsRkXEKGPv1eIMp4aLW3/r3GDLileUi\nxiPEzxmFjHglP6r4+5zEWIn4471JnxkquX0OmcLFZDl6f7+5h4sKwt6EZMhIW4wShYtx5+5/dfcP\nufuGwBbARuHzv1ZdtjpTwBARGZeA0c8g70Z03xou6jDIuS/NqZuKChnRubKGDOgQMmAyTKQFjsS+\nChehbl0C00JGp1Xv48dozMVYMLNdzex4M/sE8DeAlo3LQAFDRATM3asuw8DMzNmmzftItkZ006Ty\ncJHaPSUqG3SuPHYLU43oPn08xqDvMa0CHz9nfGXvaNB3fNraKYvvtVvJu02wgHzDBeQcMPpeWK/H\n/bOOOYp/XhrRfcqq93mEi6paL5YZ7m79Hm5mPtOv6vm4Rfaqga5bFTMzYB5wNMFCgPcRTFf7AuB7\nwLt8iP9wmFlhxf/Tn2DzzeGRR2DddQu5hIhIocwG+5sZGe0WjF7DRXzfUWm5aCdW0c2rJaPdsfE+\n/fExGRMBoF1LBqSPyxjWcNGvZoHnjo/hia4TX7ixyXCHiwqnqB5i/0DwP+Er3X1bd3+Vu88AXgXs\nAxxfZeHq7IYbgtmjFC5EZNyNbsBIG0fR6RY3zOGi2zfXzfjj/EJGlmO6hYyO4zLit1Byv7zDRe7K\nGnfRz1TMaYO/mwx3uJB+vQN4v7vPj28Mn38wfF1S3HQT7L571aUQEaneaAaMDjNAtZUIG7ULF83w\nPo91PJrxx4OHjH72TQsZ0KU1IyYZLBQuchAPGU2GP1yo9aJfuwK/afPab8LXJcVdd8GLXlR1KURE\nqjd6AaOfcJFQq3DRT8U0Swhppm/uNWS02yc5I1HaMf2EjHatFslzdCpbVrl1jyozXAwaQJNTHytc\njKO13f3xtBfC7aP3dyMnd98N229fdSlERKo3BF+r9qBDuOh1itmqw0XXyu2yVYNX4JoEP6vEInyL\nls6e+HktZFbbn0WncJF2ruSxs1g4ETJms6Blte8Fm+42Mfi7XUtG2hoXtVtIr24tF1FgyPLZGdZw\nIYNax8xeB7Qb5FezD3V93HMPbLdd1aUQEane6MwidWz4PhrhxiEOFwuZ1X0GqSwVxSzf4jbij1tb\nezrNLpUlXCSl/Tv0MsNUpF2rRWuLSA1aL/IIF80e9+/UglHW9LJVh4vk5/5szSLVCzO7B+j4h8Hd\nh/Z7+iJnkdp882AcxpZbFnJ6EZHC5TWL1Gh9E9WYuqlW3Z16MHPGgu6V3EHDRVLGlox+wkXyfJH4\neZTud3oAACAASURBVBcweyJkRK0ZUZiIgka3cDHWYy7yGJ8zqLqFC+mZu29XdRmG0RNPBNPUbrFF\n1SUREaneWLVgDFPIaFkLI8s6GFkrVo1ur08ds5KlBaiXb/z7aclISg7mDrblEy4Gbr3IK1w0e9y/\nW8AosgWj6mARSfs9UAuGxBTVgnHzzXDkkXDrrbmfWkSkNFoHIyltLv9QVGGszbfbGUQV7pkzFkxW\n+hvhi/vF7vejc7hoJG6dtBkQ36nC3Wkwd6djktJW/Q62p+3b+fhK1W3MRVxRIaDO4UKkJHffrfEX\nIiKR0QkYMJ4hI66Rcsuqy2xbaaFgkG/604JJu5DRus+YDOhu9rh/Vd2jFC5EAM0gJSISNzpdpLaJ\nvY8Ms0kNW3ep1EHfjT5O1se0vZHoZ5fn6ta9dJeKK6JrFAzw3qoMF9BbwMijq1RdggV0Dxc5dJHi\n3qd7P3DbddRFqoaK6iL1oQ/B9Onwj/+Y+6lFREqjLlKddGjJiAxbS8ZERTzZkpEm64rlPeqnO1SW\ncyZ1+7cpKlz0pTmt+nDRq2027T8gDHJsEdRyITVxzz1qwRARiYxmwIC2IaPf1aqrNiVkFBQictFj\nhbtTaEl2g6pduMj1fH0e12/3qF7CQt2CBShcSK2oi5SIyKTR7CIV16a7VKc1HuqspbtUj7rNBlVI\n96CMgSdLV6miw0Xm91/EQO7mAMfmNf6iXdepugUL6C1cqIuUxBTRRcodnvvcIGRsWsNfFxGRrPLq\nIjX6AQNGKmQU/a19pkp2rxXsnEJGpKifQSHvPYvmAMcWMbi7l9W+q9Bry4UChsQUETAefjiYQeqR\nR8D0Ly4iQ0xjMHqRobvUsCg6CHVd86KfCnbGY/oZj1GavMdaTJw3/1MOrI7doSLqFiU1FHWPUrgQ\nEQnUeNL+nF1GUDlpErRkhKtWR6tLx1eUrru8y5msxLddRXyQCnZ0bB9jRLKsIl6oota2aA54fB1W\n7i6TwsVIMrOPAF8GNnP3VeG2TwDvAlYDH3D3S8LtewDfBZ4FXOTuHwy3rwucC8wCHgLe4u5Ly3oP\n99yjNTBEROJGpwVjP7ItPJdiGNfIyNMsFk4JLVNaMnJbnbrzedq1Ki0MS1mU0luzmgMer3AhI8DM\ntgb2B+6NbdsFOBLYBTgIOMtsom3gG8Bx7r4TsJOZvSHcfhywyt13BE4HvlTSWwBg5UrYcssyrygi\nUm+jEzCSkoFjxNbGKENLyMhzdqou3Y2GsetaT5oDHq9wIaPjK8BHE9sOA85z99Xufg+wBNjTzLYE\nNnL3+eF+5wKHx445J3x8PrBvoaVOePBB2HzzMq8oIlJvoxsw0jSmblK46KywkAHFdT2qs+aAxytc\nyIgws0OBZe5+U+Kl6cCy2PMV4bbpwPLY9uXhtpZj3P0Z4BEzK20gkQKGiEir0anhNXrZt3UWKYWL\nHjRW57yw3LR6rN0xDMYpXChYjAQzuxTYIr4JcOAU4JME3aMKuXRB50314IPw6leXeUURkXobnYCR\nlcJFz6YM+s47ZKSIBt+PlOYAxypcyBBy99QAYWa7AdsBN4TjK7YGrjOzPQlaLGbEdt863LYC2CZl\nO7HX7jOztYGNowHjaebOnTvxuNFo0Gg0enlbU6gFQ0SGVbPZpNls5n7e0VkHo9s89bFvyhUu0nUb\nRD1lbESeISOlFaPMgNFx3Ece77M5wLEKF4MZknUwzGwT4L+AbYF7gCPd/dHEPusBvwXWJfiC6Hx3\nP7X3wtWLmd0N7OHuD5vZrsD3gb0Iuj5dCuzo7m5mVwPvB+YDFwJfdfeLzewEYDd3P8HMjgIOd/ej\n2lwr93UwXvYy+N73YPfdcz2tiEjptA5GrxQuOsoyQ9OUCn/BXZtqM9h70PfZHODYcQkXfcz+NoJO\nBi5z952BXwGfSO7g7n8BXufuLwdmAgeF3/oPOyfs1uTutwI/BG4FLgJOiCWCE4F5wGJgibtfHG6f\nB2xmZkuADxL8LEvz4IOw2WZlXlFEpN7GqouUwsXgCusupbEYU41DuFCoiDsMeG34+ByCaDqlouzu\nT4YP1yP4P3zom6Hd/YWJ518AvpCy30LgpSnb/0IwtW3p3OGhhxQwRETiRiZgZO1Oo3AxVa/rS7Rd\niK8AQz8Wo9nncQoX4+j57r4SwN0fMLPnp+1kZmsBC4EXAV+PTdsqFXjkEdhgA1hvvapLIiJSH+PT\nRQqFizT9Ll5X6PS1JSssLDX7PG7Uw8UYd4cys0vN7MbY7abw/tCU3VNbJtx9TdhFamtgr3DMglRE\nA7xFRKaqbQuGmX0JeBPwF+BO4Fh3f6zd/goPvRt0ZeyWlow8ukq16SY1lK0YzT6PG4dwMYyyfLZv\na8LtzY67tJtVCcDMVprZFu6+MlxU7g9dzvWYmf0aOJBgvIJUQAFDRGSqOrdgXAK8xN1nEqzkOmXA\no/Rv0HCRatCWjDbHFx0uMp2/rFaaYa2AdzMOrRa7NOCIuZO33v0UOCZ8fDRwQXIHM9vMzJ4TPl6f\nYB2J2/u5mORDAUNEZKraBgx3v8zd14RPryboDjCUCqnMDyDP8uQ2s1RF4aKn6/Ty3hp9F2V0KuL7\nMR7BIj+nAfub2R3AvsAXAcxsKzP7ebjPVsCvzWwRcA3wS3e/qJLSCqCAISKSprYBI+FdwC+qLkQ/\nosp83UJGngYOGRWHi8Ku1xjg2GGulCtU9MXdV7n7fu6+s7sf4O6PhNvvd/dDwsc3ufse7j7T3V/m\n7p+vttSiGaRERKaqdAyGmV0KbBHfRDCw8VPu/rNwn08BT7v7Dzqd67q5F0483qqxI1s1dsq/wD2K\nQkU0hmAhsyofK1JU0Mk0fW0PwaOqMRddZ8jqdaxJg/7HY+zH8IzJqFOguL8JDzSrLoWMiQcfhBe8\noOpSiIjUS61X8jazY4C/B14fznPebj8/zs8srVxZxMNFpA7rcBTdkpLHjEx1GNDd9X30EjKaAxWl\n3iGjTsGinTxW8j6nj/8nj85nNVTJV94reb/jHbDffnD00bmdUkSkMiO/kreZHQh8FDi0U7ioo7Rw\nEX9eVXepMq47aDioQ7iADOUoazwG1Gssw37UqzwiFVu1CjbdtOpSiIjUS20DBvA1YEPgUjO7zszO\nqrpAWaRW4mPfdlcdMsrQT0iYOWNBbcJFJNfyNHI6T5mV+2SYUKAQmeLxx2HjjasuhYhIvdR2HQx3\n37HqMvQqHhomWi+icBFb46GKMRl1DjR1CxZxHcdk5LH2xyDiFf5+ulEpMIgM7PHHYaONqi6FiEi9\n1DZgDLOp4YJwsO/UheTqMPC7CFlCw7AsoJdbyGgw+HiMdhQWRCrx2GMKGCIiSXXuIjVUpoy7iIeL\nlvtprfuVoK6tF8MQLiK5lbWRz2lEpB7URUpEZCoFjBx0DRdR95XoeSJk1DUASEY9r/tRSClEpALq\nIiUiMtXIdJHKUkkfxa5I3Si85CfX8RgNiusuJSKlePrp4Lb++lWXRESkXsaqBaOoynYUXCa60UTf\naDfCHaL+8dHz8PWi18VQuKi5RtUFEJFBRK0XptVORERajFXAgApDxsR9OeFChkQDBQ2RIaXuUSIi\n6Uami1SWQdNRpb6omZtmsZCFzJrsShN1m2mEO5QYLtR6UYFBpq1toC5TIkNGM0iJiKQbqxaMeAgp\nvyVDLRdjodcB3y3HotYMkSGiGaRERNKNVcCAakNGWeFCrRfFyTRd7SAhAxQyRIaEukiJiKQbmS5S\nmbqmJFbSLlKyu5TCxZgZdJXvRnjfzKEsMrhm1QWQOlIXKRGRdOPVghGr8JWxBkWyJUPdosbMoC0Z\noG5TIjWmLlIiIunGK2BAZSGjjHCh1osayiNkgIKGSA2pi5SISLoR6iKVcb8G4cxOrd2lippZCtRy\nITlqxB43KyqDiADqIiUi0s74tWBESm7JKNowl33Y9Dx+J69WjCnnRa0a7TTQz0cKpy5SIiLpRqcF\nI4tG/PFkpW/Yx0goXAyBQQd9dzx3eN8s5vRDo1F1AWTcPPEEbLtt1aUQEamf0QkYjV72HZ1wIdWY\nWEyxF0WGDBjf7lONqgsg4+rJJ2GDDaouhYhI/YxfFymFC8lJX1MdF9Vdasp1GO0uQg1G+/3JUHjq\nKVh//apLISJSP6PTgpHFCIYLdY+Srhqxx82KyjCoRtUFEJnqz3+GZz2r6lKIiNTP6ASMHr4ZHpVw\nIdWrZVepjtdOPG9WUIasGlUXQKSzP/9ZLRgiImlGJ2BkNErhQq0XQywKxFUFjYlypGxr1qAMIkPg\nqafUgiEikmasAkZffeZFuuirFSNSZWtGO40M+zQLOKfIkFELhohIuprVbPrXS3gYhdYLqZeRCxnd\nNKouwOgxs02A/wK2Be4BjnT3R1P2ew7wHWA3YA3wLne/psSiSkgtGCIi6cZuFqlRCRfqHjViyppd\nSursZOAyd98Z+BXwiTb7nQFc5O67ALsDt5VUPklQC4aISLqxChgKF1KkgbvgKWSMu8OAc8LH5wCH\nJ3cws42Bfdz9bAB3X+3uj5VXRInTNLUiIumGrF9Ge6MSHmS4DdRVCoazu5Tk5fnuvhLA3R8ws+en\n7LM98JCZnU3QerEA+IC7/7nEckpI09SKiKRTTWbIqPViDChkDKf7m/BAs+MuZnYpsEV8E+DAKSm7\ne8q2acAewInuvsDMTifoWvWZPkosA1IXKRGRdGNTi4kq5mrpkKIN3IoBChl1c1mWnRq0jn4/dcoe\n7r5/u6PNbKWZbeHuK81sS+APKbstB5a5e9Qf73zg41lKJ/ly1yBvEZF2xmIMRvxb/2FuARjmso+b\nXKZE1piMcfNT4Jjw8dHABckdwi5Uy8xsp3DTvsCtpZQuZ2b2GTNbbmbXhbcDY699wsyWmNltZnZA\nbPseZnajmS0OW2+i7eua2XnhMVeZ2Yyiy//Xv8K0abD22kVfSURk+Ix8wEirlKuiLkNDIWOcnAbs\nb2Z3EASHLwKY2VZm9vPYfu8Hvm9miwjGYfxL6SXNz7+5+x7h7WIAM9sFOBLYBTgIOMvMLNz/G8Bx\n7r4TsJOZvSHcfhywyt13BE4HvlR0wdV6ISLS3kgHjHiQWLR0dku3lWELGcNWXslxYcfGagWNMeDu\nq9x9P3ff2d0PcPdHwu33u/shsf1ucPf/3969B0tSlncc/z7sghcQlCKAsixIkM1GSNbdZCUxyoZb\nVlQwlhrKJKgRNaKJUcvES1JiFEUqRkJFkvJG8FaIaHTjrQKFR8tELi4ugqBu1MXl4lKK4CUGluXJ\nH9Nnd/YwfW7TPd3T8/1UTe2cnu6Zp8++02d+875v929n5qrMfNaga2WMkRiw7DTgkuIMWVuAzcDa\nYtjYIzLz2mK9D7LrTFv9Z+C6jF5Aq5XzLySpXGcDxsxwMei+H9pVt0qvHm/IUPe8IiI2RcT7igsI\nAhwCbO1b57Zi2SH05qBMu7VYtts2mbkDuDsi9q+zcE9RK0nlOjmLtCxc9C+b/uC3kTWtn/htENJO\nTv7WGJnlrFlvBC4E/j4zMyLeCrwTOLOql57twbPPPnvn/XXr1rFu3boFv4CnqJXUBVNTU0xNTVX+\nvJ37pFIaLqY/lBXfAo9byND4quSsUv2mezIMGmq52c6aNcN7gf8o7t8GHNr32LJiWdny/m1uj4gl\nwL6ZeVfZi/UHjMVyiJSkLpj5Jcub3/zgMyAuRqeGSM0ZLmbcH4fhUm2tSwtT6VCpaQ6Z0hgr5lRM\nexZwY3F/A3B6cWaoxwJHAtdk5g+BeyJibTHp+wx2nWlrA70zbwE8B7iy7vrvvRce8pC6X0WSxlNn\nAsa8wsWAZeMQMqRSTgDX+DqvOOXsJuA44FUAmXkTcCm90+9+DjgrM6cvOvhy4P3Ad4DN02eeKpYd\nEBGbgb+id/HBWm3fDnvuWferSNJ46swYizVs7FxA6Nr+TLrKh0r1c26GxkxmnjHLY28H3j5g+Ubg\nmAHL76V3atuR2b4d9tprlK8oSeOjMz0Y/XYbjjLo292+Zf3rOg9DY83eDGlk7rvPHgxJKtOpgNEf\nEEpDxpiEC3svuqmWuRgzGTKk2jlESpLKdSpgwBwhY0zChTQ0ezOkWhkwJKlc5wIGzBIyBiwzXGjU\nRtKLMc2QIdXCORiSVK6TAQPKQ8Y4hAuHR3XfyEOGQUOqlHMwJKlcp087039mqZkf6NoaLqTaeIE+\nqTIOkZKkcp3twZg2KEi0OVzYezE5RtqL0c8eDWloBgxJKjcRX2X292S0OVxo8tR6bYy52KMxf1vv\naroCtYxzMCSpXOd7MKatYWPrw4W9F5OpsZ6MafZoSAvmHAxJKjcxAUNqs8ZDBhg0pAVwiJQklTNg\ntIS9F2oNg4Y0JwOGJJVrfcCIiNdExAMRsX/TtUh1akUvRj+DhlTKORiSVK7VASMilgEnAbc0XUud\n7L3QtNaFDNgVNAwb0k7OwZCkcq0OGMC7gNc2XYQ0Sq0MGdMMGxLgEClJmk1rA0ZEnApszcwbmq6l\nTvZeaGwZNDTBDBiSVK7RE+BHxOXAQf2LgAT+FngDveFR/Y9JE6HR62Ms1MyQ4XU1NAGcgyFJ5Rr9\nJJCZJw1aHhFHA4cD10dEAMuAjRGxNjPvHLTNdWd/duf9R697HI9ed1T1BVfM3gvNZqxCRr/+wNFE\n2Lh5Cr41NfrX1US57z5YapaWpIFaeXjMzBuBg6d/jojvA6sz8ydl26w++2mjKE0aqbENGdMGDaGq\nO3SsXNe7TfvUm+t9PU2kHTsMGJJUZlwOj4lDpDShxj5kzNRE6JAqtmMHLFnSdBWS1E5j8Vc9M49o\nuoaqOTxKC9G5kDHTXJPFDSBqGQOGJJXzr7Y0JjofMmYzAWeriohHAR8DDgO2AM/NzHsGrPdK4Mzi\nx/dm5gUjK1I7PfCAAUOSyrT2NLVdZu+FpAFeB1yRmSuAK4HXz1whIh4PvAj4LWAV8PSI6FwP7zjY\nsQP28C+oJA3k4VEaI62+CJ+GdRpwcXH/YuCZA9ZZCVydmfdm5g7gy8CzRlSf+jhESpLKdSZgjEuv\nwLjUqfYyZHTWgZm5DSAzfwgcOGCdG4EnR8SjIuLhwCnAoSOsUQUDhiSV69QcjI2sYQ0bmy5Dqt1E\nz8dora8A/zXrGnNcXHSmfNCCzG9FxDuAy4GfA18HdiyyYA3BORiSVK5TAQMMGZochoxR2jDP9VbO\n+mjZxUUBImJbRByUmdsi4mBg4EVFM/Mi4KJim3OArfMsThVyDoYklevk4bGtw5DaWpfGl8OlOmUD\n8ILi/vOBTw9aKSJ+pfh3OfCHwEdHUZx25xApSSrXmYAx85tcP8xrUhgyOuMdwEkR8W3gBOBcgIh4\ndER8pm+9T0TEjfQCyFmZ+dPRlyoDhiSV60zAAEOGJpchY/xl5l2ZeWJmrsjMkzPz7mL5HZn59L71\nnpKZR2fmEzJzqrGCJ5wBQ5LKdSpgQHtDRlvqUHcZMqTRcZK3JJXrXMCA9oYMqW6GDGk0nOQtSeW6\nc3ic2v2EWG0KGQYcjZIhQ6qfQ6QkqVx3AgbMGTKkSWHIkOplwJCkct0KGDBryGiiJ8HeCzXFkCHV\nx4AhSeW6FzCgdSFDaoohQ6qHk7wlqVw3AwY8KGT0G1XIMMyoDQwZUvWc5C1J5bp9eOwLGW2a9C2N\nmiFDqpZDpCSpXLcDBjQWMgwwahtDhtomIv4iIm6OiBsi4ty+5a+PiM3FYyf3LV8dEd+IiO9ExPl9\ny/eKiEuKbb4aEcvrrn3vvWHPPet+FUkaT90PGDMsJmTcMfWduspp1M+mrmu6hFq4X+VaFzK++qWm\nK1BDImId8AzgmMw8BviHYvlK4LnASuCpwIUREcVm/wK8KDOPAo6KiD8olr8IuCszHwecD5xXd/3X\nXAMrVgz/PFNTU8M/ScWsaW5tqwfaV1Pb6gFrGqXJCBhDXiPjjqnNlZfUBj/v6Adx92t2rQoZVxkw\nJtjLgHMz836AzPxRsfw04JLMvD8ztwCbgbURcTDwiMy8tljvg8Az+7a5uLh/GXDCCOqvRBs/XFjT\n3NpWD7SvprbVA9Y0SpMRMGCkF+JzeJTabtXyr7UraGgSHQU8JSKuiogvRsT0gfMQYGvfercVyw4B\nbu1bfmuxbLdtMnMHcHdE7F9n8ZKkcpMTMKDVV/uWmmDIUJ0i4vJizsT07Ybi31OBpcCjMvNY4K+B\nj1f50hU+lyRpgSIzm65haBEx/jshqTaZuegPnBGxBThsEZvekpmHL/Z1uy4iPge8IzO/VPy8GTgW\neDFAZp5bLP8C8CbgFuCLmbmyWH46cFxmvmx6ncy8OiKWAHdk5oElr+vfC0maxTB/M6eVXyxijFTx\ni5CkQQwJtfkUcDzwpYg4CtgrM38cERuAj0TEP9Ib+nQkcE1mZkTcExFrgWuBM4ALiufaADwfuBp4\nDnBl2Yv690KS6teJgCFJGjsXAR+IiBuAe+kFBjLzpoi4FLgJ2A6clbu62l8O/BvwUOBzmfmFYvn7\ngQ8VvSA/Bk4f2V5Ikh6kE0OkJEmSJLXDZE3yrkBEvCYiHujKGUoi4rziYlabIuITEbFv0zUtVkSs\nj4hvFRfh+pum66lCRCyLiCsj4pvFBNm/bLqmKkXEHhFxXTEsRlq0+bz/I+KC4mJ8myJi1YzHKm+L\nw9QUEftFxMeL4/M3I+KJDdfzqoi4sZik/5GI2GvYeuZTU0SsiIj/joj/i4hXL3R/RllTXcfrYX5H\nxeMjb9tz/L9V3rYrqKny9j2Pep4XEdcXt69ExG/Md9sR1nRMsXzhbTszvc3zBiwDvgB8H9i/6Xoq\n2qcTgT2K++cCb2+6pkXuxx7A/9CbjLsnsAn4tabrqmC/DgZWFff3Ab7dhf3q279XAR8GNjRdi7fx\nvc3n/U/von2fLe4/EbhqxuOVtsVha6I3FOyFxf2lwL5N1QM8BvgevXkyAB8DzhjR7+gAYA3wFuDV\nC9m2gZoqP14PU0/Dbbu0pqrbdgX/b5W373nWcyywX3F/fd/7rcm2XVbTgtu2PRgL8y7gtU0XUaXM\nvCIzHyh+vIpeiBpHa4HNmXlLZm4HLqF38a2xlpk/zMxNxf2fAzez69z/Yy0ilgGnAO9ruhaNvfm8\n/0+jd3E+MvNqYL+IOAhqa4uLril6PclPzsyLisfuz8yfNlVP8dgSYO+IWAo8HLh9yHrmVVNm/igz\nNwL3L2J/RlpTTcfrYX5HjbXtsppqattD1VSoun3Pp56rMvOe4ser2NVWmmzbA2taTNs2YMxT9M7b\nvjUzb2i6lhr9GfD5potYpJkX5+q/CFcnRMThwCp6Z8rpgunA7kQwDWs+7/+yC/hBPW1xmJoeC/wo\nIi4qhra8JyIe1lQ9mXk78E7gB8WyuzPziiHrmW9NdWxb+/NWeLwetp6m2naZOtr2UDXV1L4XWs+Z\n7Pr81Za23V/TTvNt2waMPjH7RaHeQO9c7DtXb6jMBZtlv57Rt84bge2Z+dEGS1WJiNgHuAx4ZfHt\nwViLiKcB24pvRIIxej+pW1raFpcCq4F3Z+Zq4H+B1zVVTEQ8kt43nYfRG06yT0Q8r6l62q4tx2vb\n9vw03b4j4veBFwKtmTtaVtNC2ranqe2TmScNWh4RRwOHA9dHRNAbRrQxItZm5p0jLHFRyvZrWkS8\ngF4X6vEjKagetwHL+35eViwbe0WX7WXAhzLz003XU5EnAadGxCnAw4BHRMQHM/OMhuvSeJrP+/82\n4NAB6zybetriMDVBr8f8a8X9yxj+w8cw9ZwIfC8z7wKIiE8CvwsM+4XUMMftuo75Qz1vDcfrYeqp\n6zg7TE23Un3bHramOtr3vOopJna/B1ifmT9ZyLYjrmnBbdsejHnIzBsz8+DMPCIzH0vvDfKEcQgX\nc4mI9fS6T0/NzHubrmcI1wJHRsRhxdkfTqd38a0u+ABwU2b+U9OFVCUz35CZyzPzCHr/V1caLjSE\n+bz/N1BcayMijqU3DGJbjW1xmJq2AVujdwFCgBPoXRekkXroDR05NiIeWnzJdgK9MdjDWuhxu/8b\n+LqO+cPUBNUfrxddT8Ntu6ymOtr2UDVRT/ues56IWA58AvjTzPzuEPsyippgoW17thng3kpn4n+P\n7pxFajNwC3Bdcbuw6ZqG2Jf19M5ssBl4XdP1VLRPTwJ20Dvbw9eL/6P1TddV8T4eh2eR8jbkbdD7\nH3gp8JK+df6Z3llUrgdWD3iOStviMDUBv1l8INgEfJLizC4N1vMmeh+6vgFcDOw5it8RcBC9ceN3\nA3fR+zC4T9m2TdZU1/F6mN9RU217jv+3ytt2BTVV3r7nUc976V0Y9LqivVwz27Yj+h0NrGkxbdsL\n7UmSJEmqjEOkJEmSJFXGgCFJkiSpMgYMSZIkSZUxYEiSJEmqjAFDkiRJUmUMGJIkSZIqY8CQJEmS\nVBkDhiRJkqTKGDA09iLiyIg4sOk6JEmSZMBQS0XEQRFxTkScO4/VXwL8rO6aJEmSNDcDhlopM7cB\n1wArZ1svIh4CLMnMX/Yte2REnB0Rv4yI/4yIV/Q99uxi+YcjYnVtOyBJkjShljZdgDSLVcAVc6zz\nTODT/Qsy8+6IuBD4O+Clmfl9gIjYHzgIWJGZP6ihXkmSpIlnD4ba7HjmDhjHZeaXByw/CdjSFy6e\nBJycme82XEiSJNXHgKFWioiHAYdm5s0R8bSIeFdE/CIiom+dxwC3lzzFicDlEbEkIs4B9s7MS0ZQ\nuiRJ0kQzYKitfg/YHBF/AlwHvAZYmZnZt84fAx8u2f4E4LvAi4FTiueTJElSzQwYaqvjgV/SG+q0\nOjMfGDC06YjM3DJzw4hYARwCfDcz/xU4Dzir6BUZKCJ+NSI2Vla9JEnShDJgqK2OB14LvAX4EEBE\nHD39YEQ8Ebi6ZNuTgK9n5ieLny+ldxrbM2d5vR8D3xyyZkmSpIlnwFDrRMS+wLLM3Az8lF3zLE7o\nW+05wMdLnuJE+iaHZ+YO4Hzg1RGxW5uPiBdHxFOBtwKXV7MHkiRJk8uAoTZ6PPB5gMy8E/hKkB6O\n5AAAANRJREFURPw58BnYee2LpZn5i/6NImJNRLwNOBn49YhYXyw/AFgDLAcujYijiuWnAAdk5ueB\nh0+/piRJkhYvdp8zK7VfRPwRcGdmfnHI53k38J7MvD4iPgW8MjNvqaRISZKkCWUPhsbR8cOGi8K/\nA78TEacCW+j1nEiSJGkI9mBorETEfsDLM/NtTdciSZKkBzNgSJIkSaqMQ6QkSZIkVcaAIUmSJKky\nBgxJkiRJlTFgSJIkSaqMAUOSJElSZQwYkiRJkipjwJAkSZJUGQOGJEmSpMr8P19Neoat+8KaAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe98043090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w_GSx10.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(np.absolute(psi_GSx10[:, 0]), \n",
    "                    (len(zpsi_GSx10), psi_GSx10.shape[-1]**2))[:, np.argmax(w_GSx10.imag[0])], -zpsi_GSx10)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\psi$', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actual profile\n",
    "#### w/out lateral viscosity ($A_h=0$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi_GS0, w_GS0, psi_GS0 = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_GS.values, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_GS.values, v_GS.values, etax, etay,\n",
    "                                                                   Ah=0., num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbe98140890>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CljyNm09F/SHilx3ZrCOjg8ohpYPCo7lDMyU6l0reSmrcC0VqNuhymq5i2R5w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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe9309d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w_GS0[0].copy()\n",
    "for i in range(10):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(10):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-10,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-10,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 2, 27, 27) 48\n"
     ]
    }
   ],
   "source": [
    "print psi_GS0.shape, len(zpsi_GS0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LS9s8CeDuW83sBTPb3d3XZVp1Su64A77whbyrEBHpHgpYXSUgv2AStGl/YcrH\n6QYhPRWypC3i7s6s+Nykn5jZoe7+MNH7xgR37zezI4EfAgfkWWs1ZnYLMDG5iKgT9xl3/+94nc8A\nm939+xV2MepDp7ivTL3yCgwM6PwrEZE0KWClreembA9yOF7Y5mN2gpCemeiky7tY62fXvxDRr8Kt\n/CrclliypdJqa4DkZAtT4mXl6+xTax13f8nMbgdOBB4m6tz8V/zcYjPbZmZ7uPvzdQtvM3c/rtbz\nZnYmcDLw9sTiar+TWr+r0nNPm9l2wPha3at58+YN3g+CgCAIar+QDC1cCDNnwi675FaCiEhuwjAk\nDMPU92tFvKJ8s8zMGVeg15FrwArbeKygjcdKCnM6bicIstt1kbpYRQtYt6Zz5Xcz83Xe/JVed7eN\nI44ff9B/lOh8oGeAe4HT3X1ZYp2TgXPc/Z1m1g9cFnemXkfU1XnRzMYCvyA67+pGMzsL2NvdLzKz\n6cAt7r7fKF9ybuKL1v8rcHQyHMaTXHyX6K9sMnALcJC7u5ktBP4GWAz8DLjc3W8ys7OBw9z9bDM7\nDTjV3U+rclwv0vvuRRfB5s0aIigiAmCWzvu5JrnoOkGXHadoxy66MLtd91RntvO5+1bgXKLZ8h4i\nmrhhmZmdZWYfide5EVhpZo8BVwFnx5vvBdxuZkuJeoa/iNcF+CZwgJkNAN8Dzmjbi0rXvwO7AreY\n2f1mdiVAPAzyh0TduhuBsxOJ6BzgGmA50QQiN8XLrwFeZ2YrgL8F/r59L6M1d9wBRx+ddxUiIt1F\nHaysdG0XK8hw380K8y6gwIJsdqsuVmUF7GBJMRWpg7VpE+yxBzz9NIwfX399EZFupw6W5CDIuwBp\nWJjNbtXFEuka998P06crXImIpE0BSxoU5F2ANC3Mu4Bs6cLDIi25+25485vzrkJEpPsoYGUl16FU\nQcH3l4Yw7wLqCMpueQnT36W6WCJd4Z57FLBERLKggCV1BHkX0IGCKsuCKs9lLUx/l0UJWepiiYyK\nO9x1F7zpTXlXIiLSfXQdLKkhyLuADhQ0uU6YSRUiIrWsXh39nDo11zJERLqSOlhZKtKMa9IGwSi3\nKd2yFKa/S3WxRDpWaXigae5JEZHUKWB1rSDn7XtNkNI+0thPNWGG+xaRTqIJLkREsqMhglJBkHcB\nbRSUPQ5b3D4NyX2GKe87JNWaN6wqRqd2EcW6LpZIwd19N5x+et5ViIh0J3WwslaED59FNW7qKH8/\nYUoFBFWnxcIBAAAgAElEQVSWVVre6PZpCzI4Tpjy/kSkk7zyCjzyCBx+eN6ViIh0JwWsrha0aZsm\nlQerQobQgNq/i1rPZSGgPedqjYLOxRLpKPffD4cdBjvvnHclIiLdSUMEJSHI/hDVwlRyed0P7AGt\ndWGCdNZtNBimHkCC+GfYwj5CunKooIjUtXgxHHlk3lWIiHQvBax2GDe1ON/y56nRD+Dl61X83QXx\nz7DJIoKRi1oJBvXO+1mU2H8mQStsYfuQQnbEWqFzsUTqWrwYTjwx7ypERLqXuXveNbTMzJxxBX8d\nuQassMH1guxKSKu7UfX3GNbZMKi8uFJdtT6gvzVx/846hywpH7qW+t9C2MK2QUo1xIrQxcojYN1q\nuHvLE16bmd/hc5re7m12byrHl+yZmef9vjttGvz0p/CGN+RahohI4Zil836uDpbEgux2neaH7qrd\nwIDqQSOovB9oPEyV9NdYv55S0Eq9oxUw+pAVoi6WFIWZ/bLBVf/o7sdnWkyXev55eO45OPjgvCsR\nEeleCljtkuswwYBcZo7LqptRM2TB8NcajFirYriqFKZgWKAa279+2FMbmVCtwiEL433fGR8v2c1K\nNWgFaHZA6QJHAh+ts44BX2lDLV3p17+OZg8coymuREQyo4AlZNLBSDtclcJQQ12goPp+ysNVA12q\nUrDqGz8wbPlAf1/14wAbF04Yvq9KIStZU24BPCS1v4GiTHahLlanutvdr623kpm9rx3FdKN774U5\nzY9CFRGRJihgSfqyClfJ+8mg1WgwqRauKgz7S3arSsGqj+EBq2/8AAOMDFkDL/UN28ewoFUtZJXq\naylkBaiLJZ3M3d/R4HoaHjhKixfDGWfkXYWISHfTJBftVsjJLoL0DpFmuGpkhr6Ser/XBsNVrWA1\niyUjdruEWcMeVwtcGxfGwwkXMjQ5RrXrNrX8NxKOcrugxeMmFKGLBe3rYmmSC2lQnpNcuMNee8Gi\nRbDffrmUICJSaJrkQlISpLerLMNVpdn7kp2gWt2fOuGq/NyqSsGqdL+Pyl0riMJWeZdrgL6o09Xf\nN3LIIFTvZEELQStAnSzpdGb2RuBSYCawa2kx4O6+Y26FdbA1a2DbNth337wrERHpbgpYPSUgsw/e\naYWrWsEquazRkNVEuEoGq1K3KhmsACbct5GjuZf1s8fWfBmlzlYpkI0IWaXJL6B6J6ulIYMBzf+3\nDtG5WFIg3wd+BPwNsDHnWrrC/ffD7Nlg6nWKiGRKAavdCnXR4SCd3bQrXJWCSem5aiELhn/ArxOu\nGg1Wg8cGJsSf95Jhq1Z3KxmyIJ6BMBmyIIPzsgLUyZIONgn4bO4XjeoiS5bArFn11xMRkdZootae\nE+RdQGMqhavkz/J1ysNZ1t2ThQwGrgn3bYwCGFHISt5KyocOtk/Q5PpheocuyhcJ1TqEUnTXApot\nMEVLlsDMmXlXISLS/RSwpHVZf5Duj7pN5edKZaF80oryOkZoIGglJ8cYNtV7IxctTu0aWdKLzOxE\nM3vEzJab2aerrHO5ma0ws6VmNjNetpOZLTKzJWY2YGYXJdafYGY3m9mjZvYLM9sto/K/CPyjmT1k\nZrclbxkdr+stXaoOlohIOyhg5aEI56YUTYPnyYztX1+9i9WkwZn9Kqg21K+qhUO38qBVkl8XC3IL\nWepi5cbMxgBXACcAM4DTzeyQsnVOAqa5+0HAWcDXANx9E3CMu88immTiJDMrTW/498Ct7n4wcBtw\nQUYv4TpgJfBV4LtlN2nSunXRbdq0vCsREel+OgdL0pHmpAbJ0BR3r0qdn4GX+hjbv37oHKakateX\nKreQYSFt4KW+ERcRbkmpo8VG1s8eOxiskt2xsf3rawY8oDsuPCx5mgOscPfVAGa2AJgLPJJYZy4w\nH8DdF5nZbmY20d3Xuvsf4nV2Inqv8MQ2b4vvX0v0B/P3GdQ/E9jD3V/NYN89Z+lS+JM/gTH6WlVE\nJHP6p7YnBfkefsOqoVstZeGqj4HKQaiFLlZSxWtYNdvJSoq7WeVdrIbCXCbhKshgnx2k97pYk4En\nE4+fipfVWmdNaR0zG2NmS4BngVvcfXG8zp7uvhbA3Z8F9sygdoimsTk0o333HA0PFBFpH3WwJD3V\nuljNhIUqFwAeNqvfeKIpzyt1sWq5kxFhbOPCCdmd2/Xr6McENtI3e2QXKx8BbZ9ZsChTtktT3H0b\nMMvMxgM/MbND3f3hSqtmVMJK4GYz+zGwtqy2z2Z0zK61ZAkcc0zeVYiI9AYFrLwUarr2FKX4mkrd\nq+REEYPXlypdVyo53fmdjHqYYNISZg2bmAJg/eyxg+dV1fXrsvv9VBwmWFXmfxcBPTt9ewdcF6uR\nruny8BlWhM/UW20NkLyk7JR4Wfk6+9Rax91fMrPbgROBh4G1pWGEZjYJ+F3dgkdnF+BnwI5lNWra\n9lFYsgTOOy/vKkREeoMCVs8K8i5gSIXrVCXDVUny/rAPocnrY41SpfOwBuhrfmKKUri6L7FsYXzd\nrNkMBbdkFw6GrunVjaEbitXF6oCQVc/0YC+mB3sNPv75xUsqrbYYONDM9gOeAU4DTi9b5wbgHOAH\nZtYPvBAHp9cBm939RTMbCxxHNKtfaZszgS8BHwCuT+t1Jbn7B7PYby/auBF++1uYMSPvSkREeoPO\nwZJiqdBVmsWSYUMES4/7xg8MH943ynOxkpNN1O0eVJta/dfx7b7otmogug0+t5AR18bKV1Dn+bAN\nNUiW3H0rcC5wM/AQsMDdl5nZWWb2kXidG4GVZvYYcBVwdrz5XsDtZraUKJL+Il4XomB1nJk9CryD\noeDVsjjMpbaeRB58EA46CHbaKe9KRER6Q0sdLDO7j+iN+xbg58BrgBmJN2KppVuHCY5GIhyVDw1M\nBpNkAKp6PlYKwwQblhwOGHetSsEqBIIBmApwRDThRbKLNSLMtf1vIaBng1QXdLEa4e43AQeXLbuq\n7PG5FbYbAA6vss91wLEplpm0FhjfwHprgN0zqqHrDAzAG9+YdxUiIr2j1SGC7wWeB/4JOIboDW8V\noIAlzeuvfN5VUrIDNOJ8plJYupNRSQ4TTJ6HVXWoYNlwwFWJVcLEzzMT6yYnu+gbP8C9HD26YjtR\nkYYJQs+ErA6zs5nNb2C9HTKvpIs89JCGB4qItFNLQwTd/XF3fwH4mbt/yN1PJbrwpEh946bW/YBb\n6l4lL95bScMzAZaHr7jrVW2YYPL++tm1RyVNHcWM7sPqziV8BDkcU6SqzwOPN3BLbVhiL3jwQTjs\nsLyrEBHpHWlNcjHFzC4g6lztndI+pZslw8RbGXHNq2b0jR9g4KUa6aY0XLAU5kohqzQssRSymMBA\nf7yfKoOU+mYPRBNWLASOIOpMzWb4pBYNmMWSofDWn6ip24eNqoslNbj7xXnX0I3UwRIRaa+GA5aZ\nHe7u91d6zt2/bmYnAx8BfpxWcT2h2z9QV1LnA3b5uVeDnauFw68pVdLwxYAbCFqlWf0G+qOjVxoy\nWDFkVRHUKKePgaHa35qorxf/JvKkkCVd7IUX4MUXYd99668rIiLpaGaI4CdrPenuN7r7Oe5+a4s1\njY4+IHWGZLg6isHuVV2jmYZ9w6rKQWURwyfBuJOhsLUwum1cOIGNCycw8FIp5vWxhFmD90eYHf0o\nDRMMGixxWMcu+Tfc1i5P0MZjiUg7PfQQHHoojNGcwSIibdPMP7l/bmZVvwMzs+kp1NMahaxiKw9X\nCZVmDhyhRqeophaD1sBLfYNBqxSy1s8eOxQMjxhlXbFh52HlFrLKBdntuojduUZmnRTpQDr/SkSk\n/ZoJWH8F/GmlJ8xsDPCFVCoaue8TzewRM1tuZp+uu0EnhqxcPkiH7T1cpXBV57pVg8MDFzIUrlq5\noHApaJV/wK8TtMq7WRVDFgx2sUaln6HfR2FCVo9RyOopZvY5M3vAzJaY2U1mNinx3AVmtsLMlpnZ\n8Ynlh5vZb+L3o8sSy3c0swXxNvfU+jKy3XT+lYhI+zUcsNz9OuD7ZnZaaZmZjTWzjxPN6vTutIuL\ng9sVwAnADOB0Mzuk7oZH0ZlBq1vVClejmNxiwn0b61+0t16XpFbQKn3QLgWt8m4WQ7fBkJXoYtUa\nJlhrJkQgx5AVtOEYIo2LQ8tHzOxKM5ufvKV0iEvc/Y3uPgv4GXBRfNxDiS5B8gbgJOBKM7N4m68C\nH3b36cB0MzshXv5hYJ27HwRcBlySUo0te/BBBSwRkXZralR2fIHJJ83seDP7HPAk0blZlwHfzaC+\nOcAKd1/t7puBBcDchrdWyMpfKRwkQ2+VzlXFyS1K3av74lvZMMFK18pqSrKrlQxcVYJWKWTN54yR\n52MdQUNdrJrBEHqjk1XEYYKgLlaxXAv8LbCBkdO0t8zdX048fA2wLb5/CrDA3be4+ypgBTAn7nCN\nc/fF8XrzgVPj+3PjegGuA96RRo1peOghDREUEWm3ZmYRPNfdr3D3u8zs88DJwMeBH7r7VjN7XQb1\nTSYKcSVPEYWuxh1FZ3xo6saZ45LhqiQZrhqZ3KKShQwLMsNm42tV8r/BuKkV/3ZK07kvGR9d6HjY\nrIINGhayxkezFm5kwtA+3koU6pJ/v934N1JEmlWwKE4E9o+vtZgJM/sn4AzgBeCYePFk4J7Eamvi\nZVuI3oNKnoqXl7Z5EiB+P3zBzHaPv5TMze9/D5s2wd66eIqISFs1cx2sU8zsZ+6+Evgs8KS7f7/0\npLs/l3p1aemUkNUtqk1mUSFcVZrcYlj3KmHVAEwFOCIeatfKOU9A5fPQgqG7NcLWRiYwcPxQqHvf\n7O9FISs2Na63af1UDlkQHV8hS3rHE8BOrezAzG4BJiYXAQ58xt3/293/AfiH+PzejwPzWjle2XFy\nV+peWSGqERHpHc0ErLcCj5nZKuAW4HEz+zN3/y+IJqNw95tSrm8NkDxZeEq8bKTH5w3dnxDA7sHw\n55MfUntemHcBjYuHBw4LK/EwwdL1sJYwK7rYcH8fGxdG17GqHETCBg5Yvk4Q/Sjta9HUwWcG+vsG\nL0jcx8BQJ2u0sx02KrOQFRC9/iCDfVdQtIsOJ5VfM63cuhDWh+2ppYeY2dsTD+cD15vZV4C1yfXc\n/bZG9ufuxzV46O8RnYc1j+g9Zp/Ec6X3nWrLSTz3tJltB4yv1b2aN2/e4P0gCAiCoMEym/PII3BI\n/bOWRUR6VhiGhGGY+n6bCVhfAq4EjgOOJfq2b7KZLQFuBQ4F0g5Yi4EDzWw/4BngNOD0imtOm9fY\nHovczeqW7kTyg3OGw60m3BdddPgM5jOfM2A83Nt/9NAKpTA0+DsNyvYQlj0uf76CDauI+2hRmIvn\nFxugb2jY3xFE54sRT3gxO17WD+tnj61+La1ydybuJ/9mu+FvpFNU+/vdPRj+Jc7Ki1M75BJmpbav\nDnRNhWXlM9Q6cECrBzKzA939sfjhqcAj8f0bgO+a2aVEQ/8OBO51dzezF81sDtF70xnA5YltPkD0\nF/MXQM0AmAxYWVq+HA4+uC2HEhHpSOVfcl18cTrv580ErMvisfDfjW+Y2cEMBa5jU6koIR7Lfi5w\nM9GEHNe4+7K0j9O7QjLrVlQKWaXA8FaiYXCjOAdrcJhgbAJRyCpNdjF4LlPSoqnDH1cNXM0beKlv\ncAbEAfqiLtbCaPji1NqbDl24+KW485YcEqlwVRw6J6tt3H3/Nh7ui/H1G7cBq4GPxjU8bGY/BB4G\nNgNnu7vH25wDfBvYGbgxMWrjGuA7ZrYCeJ7oy8DcLV8Ob3tb3lWIiPSehgNWpRON3f1R4FHgCjPL\n5DpY8RtYut/BFbmL1U2qdbLuZDBklSaMYPwoZwRcOBSyABgP3+t/31DIKj+HCYYPS2s2sFQY0jZA\n3/Da+xkaJpjoXjWsl8JVkYcJJilktZ2ZXe/uI2aNNbP/cvc/a3X/7v6eGs/9M/DPFZbfByPbz+6+\niWhq90JZvhymT8+7ChGR3tPUNO11/CjFfWWvqB+WOuHDZjPKpz4vubN8xaGOTkPKpmwvTY4xiyX0\njR9gbP/6oYv3Jqc9L79G2ripQ7cWjBjWVWHK9uTwwBHdq5JeCledRl/KtNsxVZYH7SyiU23eDKtX\nwwEtD6YUEZFm1e1gmdmewAZ3r3mF1PibPek4IZl/XqnVyarQ2Rmgj6O5d8TykKjSVQNDF/Mthaxk\nF6uPgeFTnyeVAkylSU9a6WzFdQMVa69rIQUJV0Ebj9WB6k1+IS2Lr7EIsGPifskBRMP5pI5Vq2Dy\nZNippXkYRURkNBoZIjge+D/xzEjXu/svM66pfXpyqGCYz2GrhaxRnosFRF2sUpdo4cjzsUohC6gf\ntEp1lZRqHUW4WT977LAp28snt6jbqeu1zlWnDBNM0pDBLJVm6hvD8Fn7nOhaU/PaXVAn0vBAEZH8\n1A1Y8SxLf2dmOwGnmtmVwNPAf8RXue9sRQxZ3TKbYLkqISs5G1/J+tljo8kiYmGt/f6aaEhe+flY\nEE3fTt/woLWQoWGDya5Rpb+FFrtatQyb3KJUR6+Fq06mkJUJd/8ggJnd7e5fz7ueTqWAJSKSn2Ym\nudgE/AD4gZntDbzfzA4g+pjxn+7+SkY1SuZCcrn20SIGg05yNr5aQsqGCSa7WLGj77t35EWI4+tV\nDRs2WClo1bpe2mi6LM1Mza5w1XmK9uVMF3H3r5vZQUSTR+xN9MXeD919Rb6VdYbly2HGjLyrEBHp\nTc1M0z7I3Z8G/gXAzI4CLjaz7YmGEN6eYn3tUcQuVjdLhqzEeVgjZuNrRqKLRT9D16QqV2nYYLNB\nq4rkRBeDFx2usE7FyS2SFK5EMLP3AVcTXQB4NdHsfX9vZme5+/dyLa4DLF8O73533lWIiPSmUQWs\nJHdfBCxKDCG8Cljt7plM256ZooWsbh0mWJIMWa2chwVDXawaQwWHSXazFk6Ijl26BtUog9awCw0n\nrJ89tvaGpeGBpf1383/zWjrxPCzJ2j8BJyfP+zWztwLfARSw6tAQQRGR/KQ2Tbu7b3L3H7j7WcD/\nS2u/0sUSYaLUzRkx3XkLJtw31EFKhp/k/bH966M75QHvrTRs4KXKQ/9K4arhyS16NVxJZszsRDN7\nxMyWm9mnq6xzuZmtMLOlZjYzXjbFzG4zs4fMbMDM/qbCdp8ys21mtntG5Y8D7ilbthB4TUbH6xp/\n+AM89xzss0/9dUVEJH0NBywzu8jMgngoYHL5TmZ2RHKZu7+YVoFtVbQT1tv6jX7YxmNVVgoqg+cr\n9RN1pBg6Q6z0c3Ca9tkMXcx32F9hZckhiI2c81XVnUQXSi4b5jc4BJCh11Ku4uQWIikzszHAFcAJ\nwAzgdDM7pGydk4Bp7n4QcBbwtfipLcAn3X0G8CbgnOS2ZjYFOI5sp0z/N+ALZrZzfMyxwOfj5VLD\nY49F17/abru8KxER6U3NdLDmEk2Pu9bMbjCzc83soHjyi+3N7OxMKmy3ooWstgrbe7gKAbLU6YHE\n8LrZUaAKiH5O7WMoWEHdYFX1fKxyjQ5TTA4XjEPWwEsjJ7FIhqwR514tRMqpg5e2OcAKd1/t7puB\nBUT/jifNBebD4HDv3cxsors/6+5L4+UvA8uAyYntLgXOz7j+s4G/BV4ys7XAi8B5wMfM7InSLeMa\nOtLjj8O0aXlXISLSu5o5B+tCd7/JzHYF3k40sfYn4utj3QbsBFyZQY3SViFtmVGwTndu8KK9/fFF\ne+8r61pBQx2ravoYGDzG2P711SecqOVOBocSblw4IZo8Y/zI1ZLhqqIinfsn3WQy0XWjSp4iCl21\n1lkTL1tbWmBmU4GZxH+pZnYK8KS7D5hZ6kUn/GWWO+9mTzwB++2XdxUiIr2rmWnab4p/vgzcEN8w\ns/2BtwH3Z1FgLoo04UXqk10EFGE4YCXlIaWPgaGL9v46sWIjwarCxBnJUJWqsmOVjlFpRsRh3avR\nDg/MdAKUkLZN2S+FF3+hdh3wCXd/OR6mdyHR8MDB1bI4trvfkcV+e8ETT+j8KxGRPKUxi+BKYGUK\ntRRLkUJW24Vk+iG7UveqQiAa7GJx76i7VRPu21hzJr++8QPDO0vJGQVrKV1kttTFWhhN+z7QP3Q9\nr9JQx5aHBlb6fXXrLJOaTbChLwFeCpfwUri03mprgH0Tj6fEy8rX2afSOvH5ttcB33H36+PnpwFT\ngQcsal9NAe4zsznu/ru6hTchnpn2s8DpwB7uvpuZHQ9Md/cr0jxWt3nySTjyyLyrEBHpXanNIigZ\nyuUDZ5jNbiu9liqdnNLQuvWzx7Y2jXuZSp2lwdkEk5qYSRCoeD5W1Wte1epejZs6/FZrPelJ44NZ\nTJn3wcFbFYuBA81sPzPbETiNeORBwg3AGQBm1g+84O6l4YHfBB5296+UVnb3B919krsf4O77Ew07\nnJV2uIpdChwGvB/weNlDwMcyOFZXeeIJ2Hff+uuJiEg2FLBq6ekJLyCvoYTlE0YMC1mjDFqVJrpo\nePKLesrCUrVJL+p2r+oFqrYJ8y5AUuDuW4FzgZuJgskCd19mZmeZ2UfidW4EVprZY8BVxOHFzN5C\nFGzebmZLzOx+Mzux0mHIaIgg8G7gfe5+D7Atrrd0jpjUoIAlIpKvlocISicKyOVDdAPhYePCCcO6\nSaVhdoPnY923cShkpTQTX8vDBJMSQx1L+xwxNDAZyFodhtqtQwUlFfG5sweXLbuq7PG5Fba7C6g7\nybe7H9BqjTW8Stl7lJm9Hng+w2N2vE2bomtg7bVX3pWIiPQudbDqKUoXK7fORpjObkZRf3kXC6Kp\n2wfPqRpFR6tW16riMMFGJUNT4vpYo5qdsNcpMErkP4Fr44mUMLO9iK7rtSDXqgpuzRrYe29dA0tE\nJE8KWNKAsO1HTHaUkhfvLd1aDVqVzsNqSBMf/gfDVbXuVVoKMaxQJHUXEk2gNAC8FlgBPA1cnGdR\nRacZBEVE8qeA1Yiu7GIFTa4fpnjshLKL9pYr72INmzwiEbQaPUertH1p6OGoJENWsv6yLtbgrWOE\nVe6LtJ+7v+ru57n7rsBEYFz8+NW8ayuyJ5/U+VciInnTOVjShJBRT99ea/rtUkhJTHcODE553ui1\nq/pmx8P/Zg9fngxmyanTYXinbETHqRSYSvUlg1Xy9STPxUpcfHiY8s5VmpcA6MYhdZquveeZ2aFE\n/zftDqwj+r/o4VyL6gCa4EJEJH/m7vXXKjgzc45tw+soynWxUv1AHY5im6C1Q1b74HwUQ+Ek7kSN\n7V8/eF0pqHwOVXK4X/nzTQUraCxcJSVfS7LTmQxZHROuwgrLghT336ROClgbDHdveTY9M/M5o7i+\n7r32tlSOXwTx9bWuAT5ANA3800QzB+4NfAf4kHfwG5eZZVr+WWfBzJnwMU1mLyLSNLN03s/VwZJR\nCGnpg3e17kR58OiPpzzvj8JQpW5WHwM1h/s1HKyg+XBVeq5aJ6tc2gE996AtkomPEP0D0+/ui0sL\nzexI4PvAWcDX8imt+J54Ak45Je8qRER6mzpYzVIXKyFo7bC1OlkQdYESnaxyyc7W4LIKHayaoQoq\nT0LRSLhKKn8t5eftFbZrVRJWWR5kcKwmdEoXSx2s1JjZr4AvuvtPKzz3LuACd39L+ytLR9YdrBkz\nYMEC6GtsZLWIiCSogyUFENJyJwtGfogudYISXaDSeVkwFLaGXbuqZPzQ3brDAJPKg1WyvkaUv5Zk\nN6tjw1XpuSCDY4pUdShQLWXeQTRMUKpYswYm61LMIiK5UgdrNNTFKhOkU0K1LlDZeVn1lAJY3VAF\nlS/6W/X3GibuB9ULyKrrktlkFmGd54OMjtsAdbAa0mUdrBfdfbfRPl90WXawNm+GsWPh1VdhjOYI\nFhFpmjpYUiAhqXwILz83Kxlk4xkGG7Fx4YQojJWvX+saVE2Fq+TjYOSqWcyAl+tMgSG5hSzNJtiL\ndjCzY4Bqb3B636pi/XqYMEHhSkQkb3qjGo2jKE4XKzUBhZjooNKwwdH8rhsJU9WOPUxY50Cl54OR\n+0orGGQarsIM9y0yKr8Dvlnneang+edhjz3yrkJERBSwOtm4qQW6BlJIql2OSuc0ZWlU4ap83aD6\nPkcbtrr1v69IFe4+Ne8aOtW6dbD77nlXISIiClijpS5WBSGpfwivNhFGpXVSE7a4XTDyqfIaGwlc\nmYerMOP9p6THhglWnLxFpAHqYImIFIMCVqdLvYsV0HrISu4rJU29xrDK8qCFbZsR1j9Wre5WYbpW\nItJJnn9eHSwRkSLQqbCtKL/WUV4K++1+SHu7JPWO18jzaQiaWz0ZqNoWrsI2bSMi7bJunTpYIiJF\noIAlFQQp7y8k+w/nzew/bHL9NtiwquDhKmfq6onUpQ6WiEgxaIhgq4pyLlbhhgpWktxfkNF+27Gd\niEjxrFsH++yTdxUiIqIOltQQZLjvkHQ6Sa1u32vCvAsQkYyogyUiUgwKWGno6nOxggz2WS5E5wS1\nQ1iQfYyShgmK1KRzsEREikFDBKVAwsT9oMH1REQE1MESESkKdbDSoi5WykIqd7bKHxdNkHcBVYR5\nFyAiGdN1sEREikEBqxt1TcgqCSnkzH8dIyz4/pqgYYIiVWmIoIhIMShgpakoXazMBHkXIE0L8y5A\ncmRmJ5rZI2a23Mw+XWWdy81shZktNbNZieXXmNlaM/tN2fpvNLN7zGyJmd1rZkdk/TqyZGafMrNt\nZrZ7YtkF8e9kmZkdn1h+uJn9Jv59XpZYvqOZLYi3ucfM9m336/jjH2HzZnjNa9p9ZBERKaeA1a0y\nu/hwkNF+JV0hCle9zczGAFcAJwAzgNPN7JCydU4Cprn7QcBZwFcTT38r3rbcJcBF7j4LuAj4lwzK\nbwszmwIcB6xOLHsD8F7gDcBJwJVmZvHTXwU+7O7TgelmVvr9fBhYF/8eLyP6HbXVhg0wfjwMVioi\nIrlRwEpb13exQCGrljDvAihGDRnSMMFGzQFWuPtqd98MLADmlq0zF5gP4O6LgN3MbGL8+FfA+gr7\n3WoUUJsAABmNSURBVAbsFt9/LbAmg9rb5VLg/LJlc4EF7r7F3VcBK4A5ZjYJGOfui+P15gOnJra5\nNr5/HfCOTKuu4JVX1L0SESkKBaxullkXCxSyagl74NjtOo60YDLwZOLxU/GyWuusqbBOufOAL5vZ\nE0SdmgtarDMXZnYK8KS7D5Q9Ve13Mpnod1iS/H0ObuPuW4EXkkMO2+Hll2HXXdt5RBERqUbTtHe7\ncVMz/MY/QB+0iyLMuwDpHR8DPuHuPzGz9wDfJBpmVzhmdgswMbkIcOAfgAvJru62D9RTwBIRKQ4F\nrCwcBSzKu4h2CdCH+0rC+GfQxmP1kA2rMu7Q5mfjwgn1V3oghN+E9dZaAyQnW5jCyOF8a4B96qxT\n7gPu/gkAd7/OzK6pV0he3L1igDKzw4CpwAPx+VVTgPvNbA7Vf2+1flel5542s+2A8e6+rlpd8+bN\nG7wfBAFBEDTzsirSEEERkeaFYUgYhqnv19w99Z22m5k5xxbsdRQtYGV+3kqY8f67TZDCPsIU9tGq\nIL9DFylgbTDcveWuhZk5vxjFv2UnjDx+/EH/UaLzgZ4B7gVOd/dliXVOBs5x93eaWT9wmbv3J56f\nCvy3u/cllj0EnO3ud5jZO4AvuvuRzRddHGa2Ejjc3deb2aHAd4m+KpsM3AIc5O5uZguBvwEWAz8D\nLnf3m8zsbOAwdz/bzE4DTnX306ocy7N4373+erjmGrjhhtR3LSLSM8zSeT9XBysrRetiZTpUENTJ\nalaYdwHS5dx9q5mdC9xMdL7tNe6+zMzOip72q939RjM72cweA14BPlja3sy+R/Q/9h7x+VYXufu3\ngI8AX4kD3B/jx53OiYf1ufvDZvZD4GFgM1GYLCWic4BvAzsDN7r7TfHya4DvmNkK4HmgYrjKkoYI\niogUhzpYWSpSwII2zb4WtuEYUhxBvocvShergB0sKaasOlhXXw2LF8PXv576rkVEekZaHSzNIthL\n2vJhNGjDMUREJEkdLBGR4ihswDKzS8xsmZktNbMfmdn4vGtqWhGvidW2kBW04TiSvzDfw+uaWCKA\nApaISJEUNmARnTcww91nEl3osSOvtdLbAhS0RESyp1kERUSKo7ABy91vdfdt8cOFRFPidp6e7WIl\nBW0+nvQUdbFE1MESESmQwgasMh8Cfp53EV1FIUtSE+ZdgEKW9LxXXlHAEhEpilwDlpndYma/SdwG\n4p9/mljnM8Bmd/9ejqW2pohdrFwEeRcgItKVXn5ZQwRFRIoi1+tguftxtZ43szOBk4G3193Z4/OG\n7k8IYPeghcp6RObXxqokiH+GbT6udL0Nq9rXmd0SwtawPccSaYCGCIqIFEdhLzRsZicC5wNHu/um\nuhtMm5d1Sd0pl5AFujBxtwkpRIeyXSFr+yC6lbx6cfbHFKlBk1yIiBRHkc/B+ndgV+AWM7vfzK7M\nu6CWaJhgBUHeBYiIdAUNERQRKY7CdrDc/aC8a+gZuXWxQEMGJXXtHCooUhCa5EJEpDiK3MGSdsr9\nA2mQ8/GldWHeBQzRrILSY3QOlohIcShgtVPRhwkWImQFOdcgItJ5dA6WiEhxFHaIoPSyIP4Z5liD\ndLxOHSq4MO8CpNO46xwsEZEiUQer3dTFakKQdwHStDDvAkR6zqZNsP32sMMOeVciIiKggCWVKGRJ\nt9C5WNIDNDxQRKRYFLCkAwR5FyCdTCFLupwmuBARKRYFrDwUfZggFKyLBQpZ0hKFLOli6mCJiBSL\nApZUp5AloxLmXUBlClnSpTTBhYhIsShgSYcJ8i5AOplClnQhXWRYRKRYFLCktsJ1sUAhS1qyYZWC\nlnQVDREUESkWBay8dMJ5WCUKWdKNFLKkS2iSCxGRYlHAkg4W5F2AdDqFLOkC6mCJiBSLApY0ppBd\nLFDIKqow7wIa18VDBs3sRDN7xMyWm9mnq6xzuZmtMLOlZjYrsfwaM1trZr8pW/8SM1sWr/8jMxuf\n9euQ2hSwRESKRQFLGqeQJd2sy0KWmY0BrgBOAGYAp5vZIWXrnARMc/eDgLOAryae/la8bbmbgRnu\nPhNYAVyQQfnSBA0RFBEpFgWsPHXSeViFF+RdgHSD7upmzQFWuPtqd98MLADmlq0zF5gP4O6LgN3M\nbGL8+FfA+vKduvut7r4tfrgQmJJR/dIgdbBERIpFAUuaU9gulkiKuiNoTQae/P/t3X+05HV93/Hn\nC1aaGBcqJYFTFoQVl2C0h0AD26NHb0EQyBE4p41F01qjUSqQeKLHJmpyQk9+FGkbiUmI2mgj/jgr\nMRGxQcoaGVtPsrhVIUSWuFqWyIpYQFJire7Cu3/Md9nhcn/Mvfc7852Z+3ycM2fn+5nP5zvv93z3\n3pn3/Xzm+x3Yvq9pW6rP3gX6LOU1wKdWFZ1aY4ElSZPFAkszZK7rADRrDhRa019stS7J24F9VfWR\nrmNZ777/fTjssK6jkCQdsKHrADSFNp7gB06tP4P/50c5k/s/hujzcA++3Vuu117g+IHtTU3b/D7H\nLdPnKZK8GrgAOGu5vhq9/fthg+/mkjQx/JWsGTPHVJ3Bbqb1mNlZxXEVW4s5cq5/O+Cef7dQr53A\nSUmeBdwPXAK8Yl6fG4HLgY8m2Qo8UlUPDDye5nawITkPeAvwoqr63lrSUDv274enPa3rKCRJB7hE\nUKvjd7Gkvgmdza2qx4Ar6J/178vAtqraleTSJK9v+twE3JPkq8B7gMsOjE/yEeDPgS1J/ibJzzQP\n/Q7wDGB7ki8muXZ8WWkhzmBJ0mTxV7JWb2KXCs7hLJYEVXUzcPK8tvfM275ikbGvXKT9Oa0FqFbs\n22eBJUmTxBmsrnmqdknSGrhEUJImiwWW1mZilwrOdR2AJI2FSwQlabJYYEmSNMVcIihJk8UCS2vn\nLJYkdcYlgpI0WSywJI1Qr+sApJnnEkFJmiwWWJIkTTGXCErSZLHAUjtcJihpiiT51ST3Ndfy+mJz\nAeUDj701ye4ku5KcO9B+WpK/TPKVJNcMtB+WZFsz5i+SHD/OXJzBkqTJYoElSVqvfquqTmtuNwMk\nOQV4OXAKcD5wbZI0/X8feG1VbaF/AeaXNu2vBR5urhF2DXD1OJPwO1iSNFkssNSeiZ3FUrd6XQcg\nLSYLtF0EbKuq/VW1B9gNnJHkGGBjVe1s+l0HXDww5gPN/Y8BZ48u5KdyBkuSJosFltaBua4DkDSZ\nrkhye5I/SHJE03Ys8PWBPnubtmOB+wba72vanjSmqh4DHkly5EgjH+B3sCRpslhgSZJmUpLtzXem\nDtzubP59GXAtsLmqTgW+CfynNp+6xX0tyyWCkjRZ/JuX2rXxBHh0T9dRaOL0cCZR41ZV5wzZ9T8D\nn2zu7wWOG3hsU9O2WPvgmG8kORQ4vKoeXuzJrrzyyifuz83NMTc3N2SYC3OJoCStTq/Xo9frtb7f\nVFXrOx23JMVLpjSP27oOYAQmtsDqdR3AOjfXdQAjdCJVteZZiyTFxlX8Lns0rTz/epLkmKr6ZnP/\nF4CfqKpXJnku8GHgTPpL/7YDz6mqSrID+HlgJ/CnwLuq6uYklwHPq6rLklwCXFxVlyzyvNX2++6J\nJ8Kf/Rls3tzqbiVp3UnaeT/1b15qn7NYWlCP2S6yNGWuTnIq8DiwB7gUoKruSnI9cBewD7hsoCK6\nHPhD4AeAmw6ceRB4H/DBJLuBh4AFi6tRueACOPzwcT6jJGkpzmB1bRZnsGBCC6xe1wFoZgssZ7A0\nnFHMYEmS2tHWDJYnudA6Mtd1ALLIlSRJM84CS5IkSZJaYoHVpVldHghedFhL6HUdgCRJ0shYYGmd\nmes6AAEWWZIkaVZZYEnqSK/rACRJklpngdWVWV4eKEmSJK1TFlgaHb+HpWX1ug5AkiSpVRZYXXD2\nqmNzXQegJ+l1HcDMSnJekruTfCXJLy7S511Jdie5vbnw7rJjk/xckl1J7kxy1ajzkCRpmmzoOgBJ\n6hdZcx3HMFuSHAL8LnA28A1gZ5JPVNXdA33OB55dVc9JcibwbmDrUmOTzAEvA55fVfuTHDXezCRJ\nmmzOYI2bs1eSxuMMYHdV3VtV+4BtwEXz+lwEXAdQVbcBRyQ5epmxbwCuqqr9zbgHR5+KJEnTwwJr\nnCyuJshc1wHoKXpdBzBrjgW+PrB9X9M2TJ+lxm4BXpRkR5Jbk/zjVqOWJGnKzU6BdRuTXcBMcmzS\nxOh1HcB6lyH6bACeWVVbgX8LXD/akCRJmi4T/x2sJG8G/gNwVFU9vOyAA4XMmaOMaoXWc3G18QR4\ndE/XUUjTY6iflx3NbUl7geMHtjc1bfP7HLdAn8OWGHsf8CcAVbUzyeNJ/kFVPTRE4JIkzbyJLrCS\nbALOAe5d8eBJKLTWc2ElaYS2NrcDfnuhTjuBk5I8C7gfuAR4xbw+NwKXAx9NshV4pKoeSPLgEmNv\nAM4CPptkC/A0iytJkg6a9CWC7wTesqY9dLV00OJqCsx1HYAW1Os6gJlQVY8BVwC3AF8GtlXVriSX\nJnl90+cm4J4kXwXeA1y21Nhm1+8HNie5E/gI8KoxpiVJ0sRLVXUdw4KSXAjMVdWbktwDnL7YEsEk\nxcYV5DHqWS2Lqyeb6CWCva4D0KLmug5gBU6kqob5/tKSkhTc09nza/SS1KS+70rSepeklffTTpcI\nJtkOHD3YBBTwy8Db6C8PHHysHfMLoLYKLgsrqUU9pqvIkiRJ6rjAqqpzFmpP8jzgBOCOJKH/Besv\nJDmjqr614M6+d+XB+4fOwYa54QMZtuCygJLGrMdkFllDnWRCkiStQxO7RHBQs0TwtKr69iKPr2yJ\noMZropcIgssEp8Fc1wEswyWCGo5LBCVpcrW1RHDST3JxQNHmEkFJU6bXdQCSJElDmYoCq6o2D3UN\nLEkzrNd1AJIkScuaigJLkvp6XQcgSZK0JAssSVOm13UAkiRJi7LAkib+BAqSJEmaFhZYkiRJktQS\nCyxJkiRJaokFliRJkiS1xAJLkiRJklqyoesApMkwh2en02TodR2AJElaA2ewJE2hXtcBSJIkLcgC\nS5IkSZJaYoElSZIkSS2xwJIkSZKkllhgSU+Y6zoASZIkTTkLLEmSJElqiQWWJEmSJLXEAkuSJEmS\nWmKBJUmSJEktscDSaD26p+sIpHUryXlJ7k7ylSS/uEifdyXZneT2JKcuNzbJM5PckuSvk/y3JEeM\nI5dRSPJzSXYluTPJVQPtb21ek11Jzh1oPy3JXzavyTUD7Ycl2daM+Yskx487F0nS5LDAWqn9va4j\nGJ91k+uOrgMYo7u7DmCM1tNxfaokhwC/C7wU+DHgFUl+dF6f84FnV9VzgEuBdw8x9peAT1fVycBn\ngLeOIZ3WJZkDXgY8v6qeD/zHpv0U4OXAKcD5wLVJ0gz7feC1VbUF2JLkpU37a4GHm9fxGuDqsSUy\n4Xq9XtchjNV6yxfWX87mq2FYYK3UY72uIxifdZPr4Afxua6CGBMLrHXkDGB3Vd1bVfuAbcBF8/pc\nBFwHUFW3AUckOXqZsRcBH2jufwC4eLRpjMwbgKuqaj9AVT3YtF8EbKuq/VW1B9gNnJHkGGBjVe1s\n+l3HwdwHX5OPAWePIf6psN4+nK23fGH95Wy+GoYFlqQpNNd1ANPgWODrA9v3NW3D9Flq7NFV9QBA\nVX0T+JEWYx6nLcCLkuxIcmuS05v2+bnv5eBrct9A++Br8sSYqnoMeCTJkaMMXpI0uTZ0HYAkaWJk\n+S5PUa1H0ZIk24GjB5vox/vL9N//nllVW5P8BPBHwOa2nrql/UiSplCqJva9cWhJpj8JSVOtqtb8\noTrJHuBZqxj6QFUdM29fW4Erq+q8ZvuX+mHWOwb6vBu4tao+2mzfDbwYOHGxsUl2AXNV9UCzbO7W\nqjplFTF3KslNwDuq6rPN9m5gK/A6gKq6qmm/GfhV4F4Gck1yCfDiqnrDgT5VdVuSQ4H7q2rBmT3f\nryRpsrXxfj4TM1htvBCS1LWqOqHF3e0ETkryLOB+4BLgFfP63AhcDny0KcgeaQqnB5cYeyPwauAd\nwL8GPtFizON0A3AW8NkkW4DDquqhJDcCH07yW/SX/p0EfL6qKsnfJjmD/mv7KuBdzb5upP9a3Ab8\nFP2TfyzI9ytJmn0zUWBJkp6sqh5LcgVwC/3v276vqnYlubT/cL23qm5KckGSrwLfAX5mqbHNrt8B\nXJ/kNfRndV4+5tTa8l+A9ye5E/ge/YKJqroryfXAXcA+4LI6uNTjcuAPgR8Abqqqm5v29wEfbGbB\nHqJfkEqS1qmZWCIoSZIkSZPAswiuUpI3J3l8ls8UleTq5kKbtyf54ySHdx1T24a5EOssSLIpyWeS\nfLm5qOrPdx3TKCU5JMkXm+Ve0siN4qLOk24VOf/4QPueJHck+VKSz48v6tVbLt8kJyf58yT/L8mb\nVjJ2Eq0x31k8vq9scrojyeeS/KNhx06iNeY7dccXhsr5wsG8krxg2LFPUVXeVngDNgE3A/cAR3Yd\nzwjzfAlwSHP/KuDfdx1Ty/kdAnyV/kkFngbcDvxo13GNKNdjgFOb+88A/npWc21y/AXgQ8CNXcfi\nbfZvw/wuoX/R4j9t7p8J7Bh27CTe1pJzs/2/6J/FsfNcWsz3KOB04NeAN61k7KTd1pLvDB/frcAR\nzf3zpvlneC35TuPxXUHOTx+4/3xg12qPsTNYq/NO4C1dBzFqVfXpqnq82dxBv7CcJcNciHUmVNU3\nq+r25v7fAbt46jWRZkKSTcAFwB90HYvWjVFd1HmSrSVn6J/Kfpo+gyybb1U9WFVfAPavdOwEWku+\nMJvHd0dV/W2zuYOD76GzenwXyxem7/jCcDn/34HNZwCPDzt2vml7cTqX5ELg61V1Z9exjNlrgE91\nHUTLhrkQ68xJcgJwKv0zns2iA38A8QumGpdRXdR5kq0m570DfQrYnmRnkteNLMr2rOU4TeMxXmvM\ns358f5aDn4nWw/EdzBem7/jCkDknuTj9y5F8kv5n36HHDvIsggvI0henfBtwzrzHptYSub69qj7Z\n9Hk7sK+qPtJBiGpRkmcAHwPe2MxkzZQkP0n/mlC3J5ljyn8+NdPW+//NF1TV/Ul+mP4HtV1V9bmu\ng1JrZvb4Jvmn9M+4+sKuYxmHRfKd2eNbVTcANyR5IfDrPPkz/9AssBZQVQu+mEmeB5wA3JEk9JfM\nfSHJGVX1rTGG2JrFcj0gyavpL7c6aywBjdde4PiB7U1N20xKsoF+cfXBqprWaxct5wXAhUkuAH4Q\n2Jjkuqp6VcdxabYN87tkL3DcAn0OG2LsJFpLzlTV/c2//zvJx+kvwZnkD2hreb+YxveaNcU8q8e3\nOdHDe4HzqurbKxk7YdaS7zQeX1jhcaqqzyXZnP7J7FZ8jF0iuAJV9VdVdUxVba6qE+lPEf74tBZX\ny0lyHv2lVhdW1fe6jmcEnrgQa5LD6F+7ZpbPOvd+4K6q+u2uAxmVqnpbVR1fVZvpH8/PWFxpDIb5\nXXIjzbW2MnBR5yHHTqJV55zk6c1sOkl+CDgX+Kvxhb4qKz1OgzOU03iMV53vrB7fJMcDfwz8q6r6\n2krGTqBV5zulxxeGy/nZA/dPo38B+oeHGTufM1hrU8z2Mo/fof/X1e39CTt2VNVl3YbUnlr6Yqoz\npTnV6E8Ddyb5Ev3/u2+rgxdKlbRKi/0uydov6jyx1pIz/WXpH09S9D+HfLiqbukij2ENk29zAo//\nCWwEHk/yRuC5VfV303aM15Iv8MPM4PEFfgU4Eri2WcW0r6rOmMaf4bXkyxT+/MLQOf+zJK8Cvg98\nF3j5UmOXej4vNCxJkiRJLXGJoCRJkiS1xAJLkiRJklpigSVJkiRJLbHAkiRJkqSWWGBJkiRJUkss\nsCRJkiSpJRZYkiRJktQSCyxJkiRJaokFltalJCcl+ZGu45AkSdJsscDSzEhydJLfSHLVEN1fDzw6\n6pgkSZK0vlhgaWZU1QPA54FTluqX5O8Bh1bVdwfa/n6SK5N8N8ktSa4YeOyfN+0fSnLayBKQJEnS\n1NvQdQBSy04FPr1Mn4uBTww2VNUjSa4FfgW4tKruAUhyJHA0cHJV/c0I4pUkSdIMcQZLs+Ysli+w\nXlxV/32B9nOAPQPF1QuAc6vq9yyuJEmSNAwLLM2MJD8IHFdVu5L8ZJJ3JvlOkgz0+YfANxbZxUuA\n7UkOTfIbwA9V1bYxhC5JkqQZYYGlWfJCYHeSfwl8EXgzcEpV1UCfnwY+tMj4s4GvAa8DLmj2J0mS\nJA3NAkuz5Czgu/SX+p1WVY8vsLRvc1XtmT8wycnAscDXqurdwNXAZc2s2IKSPDvJF1qLXpIkSVPP\nAkuz5CzgLcCvAR8ESPK8Aw8mORO4bZGx5wBfqqo/abavp38a959d4vkeAr68xpglSZI0QyywNBOS\nHA5sqqrdwP/h4Peszh7o9lPAHy2yi5cwcHKMqnoMuAZ4U5In/ZwkeV2S84FfB7a3k4EkSZJmgQWW\nZsWPAZ8CqKpvAZ9L8m+A/wpPXPtqQ1V9Z3BQktOT/CZwLvDcJOc17UcBpwPHA9cn2dK0XwAcVVWf\nAp5+4DklSZIkgDz5+//SbEryL4BvVdWta9zP7wHvrao7ktwAvLGq7m0lSEmSJE09Z7C0Xpy11uKq\n8XHgnyS5ENhDf+ZMkiRJApzB0jqQ5Ajg8qr6za5jkSRJ0myzwJIkSZKklrhEUJIkSZJaYoElSZIk\nSS2xwJIkSZKkllhgSZIkSVJLLLAkSZIkqSUWWJIkSZLUEgssSZIkSWqJBZYkSZIkteT/A/nBz1XA\nsWJlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe92dba090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w_GS0.imag[0]*24*3600., 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(np.absolute(psi_GS0[:, 0]), (len(zpsi_GS0), \n",
    "                                psi_GS0.shape[-1]**2))[:, np.nanargmax(w_GS0.imag[0])], -zpsi_GS0)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$|\\hat{\\psi}(z)|$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### w/ lateral viscosity ($A_h=10$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zpsi_GS10, w_GS10, psi_GS10 = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_GS.values, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_GS.values, v_GS.values, etax, etay,\n",
    "                                                                   Ah=1e1, num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbe933e4790>"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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crzVQgy6Yjhd4eHWe9epfNAv+VaYgoyJ9wrUjbhvxJxF/3BOWPV46/L0ed7/HnQy47YBr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\nkAXfmBn6/63Z7RPgn1IxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe93090150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w_GS10[0].copy()\n",
    "for i in range(11):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(11):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-11,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-11,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Cz38+EqRWrIBddx0dpPbdN1l018x6b9gC1p8BrwW2zTtg5RauhjlYQZcrWEUELAer5vRx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe93751d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w_GS10.imag[0]*24*3600., 20, vmin=0.)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(np.absolute(psi_GS10[:, 0]), \n",
    "                    (len(zpsi_GS10), psi_GS10.shape[-1]**2))[:, np.nanargmax(w_GS10.imag[0])], -zpsi_GS10)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$|\\hat{\\psi}(z)|$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ACC = np.array([141.5, -51.5])\n",
    "u_ACC = Ug_coinT.sel(Longitude_t=ACC[0], Latitude_Ug=ACC[1])\n",
    "v_ACC = Vg_coinT.sel(Longitude_Vg=ACC[0], Latitude_t=ACC[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "absS_ACC = absS_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "consT_ACC = consT_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "rho_ACC = rho_anom.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "potrho_ACC = potrho_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N2_ACC = N2_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "zN2_ACC = zN2_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'phihyd' (Depth_c: 50)>\n",
      "array([ 0.19442396,  0.19423752,  0.19404019,  0.19381764,  0.19354731,\n",
      "        0.19321709,  0.19282721,  0.19239021,  0.19192699,  0.19144385,\n",
      "        0.19093698,  0.19039882,  0.1898099 ,  0.18913257,  0.18830243,\n",
      "        0.1872185 ,  0.18573511,  0.1836593 ,  0.18076118,  0.17680247,\n",
      "        0.17159525,  0.16507992,  0.15733435,  0.14850737,  0.13881034,\n",
      "        0.12853481,  0.11801354,  0.10756666,  0.09745902,  0.08788671,\n",
      "        0.07897827,  0.07078503,  0.06326259,  0.05627808,  0.04965863,\n",
      "        0.04327288,  0.03708028,  0.03109978,  0.02532855,  0.01965354,\n",
      "        0.013532  ,  0.00757065,  0.002868  ,  0.00188079,         nan,\n",
      "               nan,         nan,         nan,         nan,         nan])\n",
      "Coordinates:\n",
      "  * Depth_c      (Depth_c) float32 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 ...\n",
      "    Latitude_Ug  float32 -51.5\n",
      "    Longitude_t  float32 141.5 <xarray.DataArray 'phihyd' (Depth_c: 50)>\n",
      "array([-0.03994213, -0.03996682, -0.03998568, -0.0399932 , -0.0399786 ,\n",
      "       -0.03993611, -0.03986362, -0.03977825, -0.03970139, -0.0396303 ,\n",
      "       -0.0395567 , -0.039477  , -0.0393879 , -0.03928519, -0.03915947,\n",
      "       -0.03898747, -0.038746  , -0.03839646, -0.03788303, -0.03715682,\n",
      "       -0.03618904, -0.03498093, -0.03354391, -0.03189817, -0.0300775 ,\n",
      "       -0.0281359 , -0.02614269, -0.02416438, -0.02225243, -0.02044193,\n",
      "       -0.01875503, -0.01720081, -0.01577182, -0.01444463, -0.01317796,\n",
      "       -0.0119475 , -0.0107483 , -0.00956331, -0.00839568, -0.00729128,\n",
      "       -0.00558931, -0.00314571, -0.00097004, -0.00258657,         nan,\n",
      "               nan,         nan,         nan,         nan,         nan])\n",
      "Coordinates:\n",
      "  * Depth_c       (Depth_c) float32 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 ...\n",
      "    Latitude_t    float32 -51.5\n",
      "    Longitude_Vg  float32 141.5 <xarray.DataArray (Depth_N2: 49)>\n",
      "array([  1.56614633e-06,   2.30960472e-06,   3.98741745e-06,\n",
      "         8.21571101e-06,   1.31131955e-05,   1.55280899e-05,\n",
      "         1.39889808e-05,   1.03105396e-05,   8.78520858e-06,\n",
      "         9.20224599e-06,   8.92227392e-06,   7.83525025e-06,\n",
      "         6.88891538e-06,   6.34761928e-06,   5.82452781e-06,\n",
      "         5.36528898e-06,   6.39259837e-06,   8.11200851e-06,\n",
      "         8.98218708e-06,   8.50278153e-06,   7.93200188e-06,\n",
      "         7.62224820e-06,   7.37058374e-06,   7.06784160e-06,\n",
      "         6.68425393e-06,   6.25867045e-06,   5.82683348e-06,\n",
      "         5.40657685e-06,   4.98165505e-06,   4.53704615e-06,\n",
      "         4.05813931e-06,   3.58149852e-06,   3.13380111e-06,\n",
      "         2.73099689e-06,   2.37669931e-06,   2.05310184e-06,\n",
      "         1.74647844e-06,   1.47116548e-06,   1.24720196e-06,\n",
      "         1.13847531e-06,   1.41499174e-06,   1.58314400e-06,\n",
      "                    nan,              nan,              nan,\n",
      "                    nan,              nan,              nan,\n",
      "                    nan])\n",
      "Coordinates:\n",
      "  * Depth_N2     (Depth_N2) float32 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.01 ...\n",
      "    Latitude_t   float32 -51.5\n",
      "    Longitude_t  float32 131.5\n"
     ]
    }
   ],
   "source": [
    "print u_ACC, v_ACC, N2_ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XBaTBu8Ap4EqgJvClma11zq33tiwREcnGlE0iIuI3yiYREUkzc855XcMFmVke4BBQ1Tm3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\n4H4CcxguAoY758alsC9lk3j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kbNy4kdtuu42vv/6aokWLEhkZSdeuXTXfoIiQ40IrmNlS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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe981b0650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,5))\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "ax1.plot(potrho_ACC.values, -z_t.values, 'k', lw=2)\n",
    "ax1.set_title('Potential density', fontsize=14, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "ax2.plot(u_ACC.values, -z_t.values, 'k', lw=2, label=r'$u$')\n",
    "ax2.plot(v_ACC.values, -z_t.values, 'k--', lw=2, label=r'$v$')\n",
    "ax2.set_title('Horizontal velocities', fontsize=14, y=1.03)\n",
    "# ax2.set_xlabel('lon', fontsize=12)\n",
    "# ax2.set_ylabel('lat',fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "ax3.plot(N2_ACC.values, zN2_ACC.values, 'k', lw=2)\n",
    "ax3.set_title(r'$N^2$', fontsize=16, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zphi_ACC, Rd_ACC, vd_ACC = baroclinic.neutral_modes_from_N2_profile(-zN2_ACC.values, \n",
    "                                                        N2_ACC.values, f0_meta.sel(Latitude_t=ACC[1]).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez('OCCA_ACC',\n",
    "        absolute_salinity=absS, conservative_temperature=consT,\n",
    "        potential_density=potrho_meta, \n",
    "        z_N2=zN2_meta, N2=N2_meta,\n",
    "        u_at_Tpoints=u_coinT, v_at_Tpoints=v_coinT\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.17168641e+10   1.69315889e+04   8.10726398e+03   5.46072551e+03\n",
      "   4.12684437e+03   3.33765244e+03] (43,) [-0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857 -0.15249857\n",
      " -0.15249857] [-0.20297535 -0.20296258 -0.20293747 -0.20287247 -0.20269389 -0.2023377\n",
      " -0.20183172 -0.20129993 -0.20085134 -0.20041873 -0.19990649 -0.19933816\n",
      " -0.19875064 -0.19811616 -0.19736217 -0.19642936 -0.19523347 -0.19321755\n",
      " -0.18958983 -0.18394816 -0.17658882 -0.16736786 -0.15582094 -0.14174616\n",
      " -0.12531485 -0.107045   -0.08759488 -0.06763174 -0.04776133 -0.02856812\n",
      " -0.01056157  0.00587186  0.020696    0.03413756  0.04651758  0.0580584\n",
      "  0.06869688  0.07811508  0.08600607  0.09222893  0.09699958  0.10110842\n",
      "  0.10272527]\n"
     ]
    },
    {
     "data": {
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0CcthXlvzGk8seYIzBp7Br878FYPTB7fvQ7SAUNQ8SnMiCZbVn0eqIxizjU1F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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe9b1fbf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print Rd_ACC, zphi.shape, vd[:, 0], vd[:, 1]\n",
    "plt.figure()\n",
    "for i in range(vd.shape[1]):\n",
    "    plt.plot(vd[:, i], -zphi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dx_ACC = gsw.earth.distance([ACC[0]-.5,ACC[0]+.5], [ACC[1],ACC[1]])[0][0]\n",
    "dy_ACC = gsw.earth.distance([ACC[0],ACC[0]], [ACC[1]-.5,ACC[1]+.5])[0][0]\n",
    "# dx = 4e3; dy = 4e3\n",
    "Nx_ACC = 100\n",
    "Ny_ACC = 100\n",
    "k = 2*np.pi*fft.fftshift( fft.fftfreq(Nx_ACC, dx_ACC/2e1) )\n",
    "l = 2*np.pi*fft.fftshift( fft.fftfreq(Ny_ACC, dx_ACC/2e1) )\n",
    "\n",
    "k_ACC = k[np.absolute(k) < 5.*Rd_ACC[1]**-1]\n",
    "l_ACC = l[np.absolute(l) < 5.*Rd_ACC[1]**-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69219.9312463\n",
      "[ -2.90468260e-04  -2.72313994e-04  -2.54159727e-04  -2.36005461e-04\n",
      "  -2.17851195e-04  -1.99696929e-04  -1.81542662e-04  -1.63388396e-04\n",
      "  -1.45234130e-04  -1.27079864e-04  -1.08925597e-04  -9.07713312e-05\n",
      "  -7.26170650e-05  -5.44627987e-05  -3.63085325e-05  -1.81542662e-05\n",
      "   0.00000000e+00   1.81542662e-05   3.63085325e-05   5.44627987e-05\n",
      "   7.26170650e-05   9.07713312e-05   1.08925597e-04   1.27079864e-04\n",
      "   1.45234130e-04   1.63388396e-04   1.81542662e-04   1.99696929e-04\n",
      "   2.17851195e-04   2.36005461e-04   2.54159727e-04   2.72313994e-04\n",
      "   2.90468260e-04] 0.00029530601258\n",
      "(33,)\n"
     ]
    }
   ],
   "source": [
    "print dx_ACC\n",
    "print k_ACC, 5.*Rd_ACC[1]**-1\n",
    "print k_ACC.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### w/out lateral viscosity ($A_h=0$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.56614633e-06   2.30960472e-06   3.98741745e-06   8.21571101e-06\n",
      "   1.31131955e-05   1.55280899e-05   1.39889808e-05   1.03105396e-05\n",
      "   8.78520858e-06   9.20224599e-06   8.92227392e-06   7.83525025e-06\n",
      "   6.88891538e-06   6.34761928e-06   5.82452781e-06   5.36528898e-06\n",
      "   6.39259837e-06   8.11200851e-06   8.98218708e-06   8.50278153e-06\n",
      "   7.93200188e-06   7.62224820e-06   7.37058374e-06   7.06784160e-06\n",
      "   6.68425393e-06   6.25867045e-06   5.82683348e-06   5.40657685e-06\n",
      "   4.98165505e-06   4.53704615e-06   4.05813931e-06   3.58149852e-06\n",
      "   3.13380111e-06   2.73099689e-06   2.37669931e-06   2.05310184e-06\n",
      "   1.74647844e-06   1.47116548e-06   1.24720196e-06   1.13847531e-06\n",
      "   1.41499174e-06   1.58314400e-06              nan              nan\n",
      "              nan              nan              nan              nan\n",
      "              nan] [ 0.19442396  0.19423752  0.19404019  0.19381764  0.19354731  0.19321709\n",
      "  0.19282721  0.19239021  0.19192699  0.19144385  0.19093698  0.19039882\n",
      "  0.1898099   0.18913257  0.18830243  0.1872185   0.18573511  0.1836593\n",
      "  0.18076118  0.17680247  0.17159525  0.16507992  0.15733435  0.14850737\n",
      "  0.13881034  0.12853481  0.11801354  0.10756666  0.09745902  0.08788671\n",
      "  0.07897827  0.07078503  0.06326259  0.05627808  0.04965863  0.04327288\n",
      "  0.03708028  0.03109978  0.02532855  0.01965354  0.013532    0.00757065\n",
      "  0.002868    0.00188079         nan         nan         nan         nan\n",
      "         nan         nan]\n"
     ]
    }
   ],
   "source": [
    "print N2_ACC.values, u_ACC.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/takaya/oceanmodes/oceanmodes/baroclinic.py:377: UserWarning: N2 is shorter than horizontal velocities by more than one element\n",
      "  # make sure z is increasing\n"
     ]
    }
   ],
   "source": [
    "zpsi_ACC0, w_ACC0, psi_ACC0 = baroclinic.instability_analysis_from_N2_profile( -zN2_ACC.values, \n",
    "                                                                   N2_ACC.values, f0_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   k_ACC, l_ACC, z_t.values, u_ACC.values, v_ACC.values, etax, etay,\n",
    "                                                                   Ah=0., num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(44,) (43, 2, 33, 33)\n"
     ]
    }
   ],
   "source": [
    "print zpsi_ACC0.shape, psi_ACC0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbe935a8b50>"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EoitkdUt7Au2AtOOTtTzywKXwHNSR+vv21fn6bl7gJTlBkhFGCa1pQj7NCKbgzcCd1ifj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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe9395d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w_ACC0[0].copy()\n",
    "for i in range(14):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(14):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-14,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-14,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8JBxySHQj4Llzo8UrNtww6+pEpNflumluZusR/RIbDiwFfu7u5zZ8wkbD0liy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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe93362950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_ACC*Rd_ACC[1], l_ACC*Rd_ACC[1], w_ACC0.imag[0]*24*3600, 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(np.absolute(psi_ACC0[:, 0]), \n",
    "                    (psi_ACC0.shape[0], psi_ACC0.shape[-1]**2))[:, np.nanargmax(w_ACC0.imag[0])], -zpsi_ACC0[:-1])\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$|\\hat{\\psi}(z)|$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### w/ lateral viscosity ($A_h=10$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi_ACC10, w_ACC10, psi_ACC10 = baroclinic.instability_analysis_from_N2_profile( -zN2_ACC.values, \n",
    "                                                                   N2_ACC.values, f0_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   k_ACC, l_ACC, z_t.values, u_ACC.values, v_ACC.values, etax, etay,\n",
    "                                                                   Ah=1e1, num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbe9304be50>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rsFf5+35M+n4fv0jBbQCb3Ar9CH9g0Vr8tbPgXzTzd7RqphqARqA9cH2h7gtVXygHvmgn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe9ffcdf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w_ACC10[0].copy()\n",
    "for i in range(14):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(14):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-14,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-14,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ww8w2BfoqXIlI1ixeHIybuvdemD49mDp9ww2DFqojjoBrroE11ki6SukUClgimaDWq7xz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\nZmZmZmaWEydYZmZmZmZmOfk/eXS+j43X5nAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe9331ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_ACC*Rd_ACC[1], l_ACC*Rd_ACC[1], w_ACC10.imag[0]*24*3600, 20, vmin=0.)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(np.absolute(psi_ACC10[:, 0]), \n",
    "                    (psi_ACC10.shape[0], psi_ACC10.shape[-1]**2))[:, np.nanargmax(w_ACC10.imag[0])], -zpsi_ACC10[:-1])\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$|\\hat{\\psi}(z)|$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2 (oceanmodes)",
   "language": "python",
   "name": "oceanmodes"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
